U.S. patent application number 10/610679 was filed with the patent office on 2004-02-05 for thematic segmentation of speech.
Invention is credited to Kubala, Francis, Srivastava, Amit.
Application Number | 20040024598 10/610679 |
Document ID | / |
Family ID | 30003990 |
Filed Date | 2004-02-05 |
United States Patent
Application |
20040024598 |
Kind Code |
A1 |
Srivastava, Amit ; et
al. |
February 5, 2004 |
Thematic segmentation of speech
Abstract
A thematic segmentation tool generates indications of
thematically coherent segments within a document. The thematic
segmentation tool includes a transcription component, a speaker
boundary detection component, a linguistic detection component, and
a topic classification component. Each of these components
generates input information for a thematic decision component,
which generates the thematic segmentation information. Multiple
thematic segments may concurrently apply to a portion of a
document. Additionally, the thematic segments may be hierarchical
thematic segments.
Inventors: |
Srivastava, Amit; (Waltham,
MA) ; Kubala, Francis; (Boston, MA) |
Correspondence
Address: |
Leonard C. Suchyta
c/o Christian Andersen
Verizon Corporate Services Group Inc.
600 Hidden Ridge, HQE03H01
Irving
TX
75038
US
|
Family ID: |
30003990 |
Appl. No.: |
10/610679 |
Filed: |
July 2, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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60394064 |
Jul 3, 2002 |
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60394082 |
Jul 3, 2002 |
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60419214 |
Oct 17, 2002 |
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Current U.S.
Class: |
704/235 |
Current CPC
Class: |
G10L 15/26 20130101;
Y10S 707/99943 20130101; H04M 2201/42 20130101; H04M 2203/305
20130101; H04M 2201/60 20130101; G10L 25/78 20130101 |
Class at
Publication: |
704/235 |
International
Class: |
G10L 015/26 |
Goverment Interests
[0002] The U.S. Government may have a paid-up license in this
invention and the right in limited circumstances to require the
patent owner to license others on reasonable terms as provided for
by the terms of Contract No. N66001-00-C-8008 (Defense Advanced
Research Projects Agency (DARPA)).
Claims
What is claimed:
1. A thematic segmentation tool comprising: a transcription
component configured to receive spoken audio information and
convert the spoken audio information into a document of text
corresponding to the spoken audio information; a linguistic
detection component configured to generate linguistic information
corresponding to the document produced by the transcription
component based on the document and the spoken audio information; a
topic classification component configured to generate topics
relevant to the document; and a thematic decision component
configured to generate indications of a plurality of thematic
segments based on the linguistic information, the document, and the
topics.
2. The thematic segmentation tool of claim 1, wherein the thematic
segments include a hierarchical arrangement of multiple thematic
segments.
3. The thematic segmentation tool of claim 1, wherein the thematic
segments include multiple concurrent thematic segments generated
for a portion of the document.
4. The thematic segmentation tool of claim 1, wherein the
linguistic information includes visible linguistic information.
5. The thematic segmentation tool of claim 4, wherein the visible
linguistic information includes at least one of periods and
commas.
6. The thematic segmentation tool of claim 1, wherein the
linguistic information includes non-visible linguistic
information.
7. The thematic segmentation tool of claim 6, wherein the
non-visible linguistic information includes phrasal boundary
information.
8. The thematic segmentation tool of claim 1, wherein the topic
classification component generates topics after training for topic
generation using an unsupervised topic discovery mechanism.
9. The thematic segmentation tool of claim 1, wherein the thematic
segments begin and end on linguistic boundaries.
10. The thematic segmentation tool of claim 1, further including: a
speaker boundary detection component configured to generate
indications of speaker boundaries for the spoken audio information,
wherein the thematic decision component uses the indications of
speaker boundaries when generating the indications of the plurality
of thematic segments.
11. A method for determining thematically coherent segments within
a document, the method comprising: receiving a document having
associated linguistic information describing linguistic features of
the document; and generating indications of thematically coherent
segments within the document that occur at the linguistic features
in the document.
12. The method of claim 11, further comprising: generating the
document by transcribing speech.
13. The method of claim 12, wherein the document is additionally
associated with topic information that summarizes topics relevant
to the document.
14. The method of claim 13, wherein the document is additionally
associated with speaker boundary information.
15. The method of claim 11, wherein the thematically coherent
segments include a hierarchical arrangement of thematic
segments.
16. The method of claim 11, wherein the thematically coherent
segments include multiple concurrent thematic segments generated
for a portion of the document.
17. The method of claim 11, wherein the linguistic information
includes visible linguistic information.
18. The method of claim 17, wherein the visible linguistic
information includes at least one of periods and commas.
19. The method of claim 11, wherein the linguistic information
includes non-visible linguistic information.
20. The method of claim 19, wherein the non-visible linguistic
information includes phrasal boundary information.
21. The method of claim 11, wherein the thematically coherent
segments begin and end on linguistic boundaries defined by the
linguistic information.
22. A computing device comprising: a processor; and a computer
memory coupled to the processor and containing programming
instructions that when executed by the processor cause the
processor to: associate linguistic information with a document, the
linguistic information demarcating linguistic breaks within the
document, generate, at a plurality of the linguistic breaks within
the document, indications of thematically coherent segments, and
output the thematically coherent segments associated with labels
describing thematic content of the thematically coherent
segments.
23. The computing device of claim 22, wherein the program
instructions, when executed by the processor, additionally cause
the processor to: generate the document by transcribing speech.
24. The computing device of claim 22, wherein the document is
additionally associated with topic information that summarizes
topics relevant to the document.
25. The computing device of claim 22, wherein the document is
additionally associated with speaker boundary information.
26. The computing device of claim 22, wherein the thematically
coherent segments include a hierarchical arrangement of thematic
segments.
27. The computing device of claim 22, wherein the thematically
coherent segments include multiple concurrent thematic segments
generated for a portion of the document.
28. The computing device of claim 22, wherein the linguistic
information includes visible linguistic information.
29. The computing device of claim 28, wherein the visible
linguistic information includes at least one of periods and
commas.
30. The computing device of claim 22, wherein the linguistic
information includes non-visible linguistic information.
31. The computing device of claim 30, wherein the non-visible
linguistic information includes phrasal boundary information.
32. A device comprising: means for associating linguistic
information with a document, the linguistic information demarcating
linguistic breaks within the document; and means for generating, at
a plurality of the linguistic breaks within the document,
indications of thematically coherent segments.
33. The device of claim 32, wherein the document is additionally
associated with speaker boundary information and with topic
information that summarizes topics relevant to the document.
34. A computer-readable medium containing program instructions for
execution by a processor, the program instructions, when executed
by the processor, cause the processor to perform a method
comprising: obtain a document having associated linguistic
information describing linguistic features of the document; and
generate indications of thematically coherent segments within the
document that occur at the linguistic features in the document.
35. The computer-readable medium of claim 34, wherein the document
is additionally associated with speaker boundary information and
with topic information that summarizes topics relevant to the
document.
Description
RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C. .sctn. 119
based on U.S. Provisional Application Nos. 60/394,064 and
60/394,082 filed Jul. 3, 2002 and Provisional Application No.
60/419,214 filed Oct. 17, 2002, the disclosures of which are
incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0003] A. Field of the Invention
[0004] The present invention relates generally to speech processing
and, more particularly, to the segmentation of speech based on
thematic classification.
[0005] B. Description of Related Art
[0006] Speech has not traditionally been valued as an archival
information source. As effective as the spoken word is for
communicating, archiving spoken segments in a useful and easily
retrievable manner has long been a difficult proposition. Although
the act of recording audio is not difficult, automatically
transcribing and indexing speech in an intelligent and useful
manner can be difficult.
[0007] Speech is typically received into a speech recognition
system as a continuous stream of words without breaks. In order to
effectively use the speech in information management systems (e.g.,
information retrieval, natural language processing, real-time
alerting), the speech recognition system initially processes the
speech to generate a formatted version of the speech. For example,
the speech may be transcribed and linguistic information, such as
sentence structures, may be associated with the transcription.
[0008] In addition to segmenting speech segments based on
linguistic information, it may be desirable to also segment the
speech based on thematic structure. For example, when archiving a
continuous broadcast of a radio news program, it may be desirable
to know the portions of the news program that discussed the weather
and the portions that were about foreign affairs. The portion of
the broadcast that was directed to foreign affairs may be further
classified into European and Middle East news segments. Users can,
thus, later browse or listen to an archive copy of the news
broadcast based on topics of interest.
[0009] One technique for segmenting a continuous stream of speech
based on thematic elements involves making thematic boundary
decisions based on a word count within a moving window of text.
FIG. 1 is a block diagram illustrating this technique in additional
detail. Initial input audio information is transcribed by
transcription component 101. The transcription may be performed
manually or automatically. Transcription component 101 outputs a
continuous stream of text. Windowing component 102 segments the
text into chunks of texts of a predetermined length (e.g., 200
words) and generates a vector of the words that occur within the
window. Words that occur more frequently within the window are
weighted more heavily in the vector. Boundary decision component
103 detects changes in thematic segments based on the word count
weighted vectors.
[0010] A problem with this technique is that it can produce
erroneous or non-optimal thematic segments. Accordingly, there is a
need in the art to improve thematic segmentation of speech.
SUMMARY OF THE INVENTION
[0011] Systems and methods consistent with the principles of this
invention provide a thematic segmentation tool that acts on text
augmented with additional information extracted from the spoken
version of the text. The thematic segmentation tool may generate
overlapping thematic segments for a single portion of text.
[0012] One aspect of the invention is directed to a thematic
segmentation tool that includes a transcription component
configured to receive spoken audio information and to convert the
spoken audio information into a document of text corresponding to
the audio information. A linguistic detection component generates
linguistic information corresponding to the text produced by the
transcription component. A topic classification component generates
topics relevant to the document. A thematic decision component
generates indications of thematic segments based on the linguistic
information, the document, and the topics.
[0013] Another aspect of the invention is directed to a method for
determining thematically coherent segments within a document. The
method comprises receiving a document having associated linguistic
information that describes linguistic features of the document and
generating indications of thematically coherent segments within the
document that occur at the linguistic features in the document.
[0014] Yet another aspect of the invention is directed to a
computing device comprising a processor and a computer memory
coupled to the processor. The computer memory contains program
instructions that when executed by the processor associate
linguistic information with a document. The linguistic information
demarcates linguistic breaks within the document. The program
instructions additionally generate, based on the linguistic breaks
within the document, indications of thematically coherent segments,
and output the thematically coherent segments associated with
labels describing thematic content of the thematically coherent
segments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate the invention
and, together with the description, explain the invention. In the
drawings,
[0016] FIG. 1 is a block diagram illustrating thematic segmentation
using a conventional technique based on a word count within a
moving window of text;
[0017] FIG. 2 is a diagram illustrating an exemplary system in
which concepts consistent with the invention may be
implemented;
[0018] FIG. 3 is a block diagram illustrating software elements in
a thematic segmentation tool consistent with the invention; and
[0019] FIG. 4 is a diagram illustrating exemplary thematic segments
for a document.
DETAILED DESCRIPTION
[0020] The following detailed description of the invention refers
to the accompanying drawings. The same reference numbers may be
used in different drawings to identify the same or similar
elements. Also, the following detailed description does not limit
the invention. Instead, the scope of the invention is defined by
the appended claims and equivalents of the claim limitations.
[0021] Thematic segmentation of spoken audio is performed by a
thematic segmentation tool on a transcribed version of the audio
supplemented with additional information that further describes the
audio. In one implementation, the transcription is supplemented
with visible linguistic structural information, such as sentence
demarcations and non-visible linguistic structural information
such, as phrasal boundaries, topic lists, and speaker boundaries.
The result of the thematic segmentation includes hierarchical and
potentially overlapping thematic segments.
System Overview
[0022] Thematic segmentation, as described herein, may be performed
on one or more processing devices or networks of processing
devices. FIG. 2 is a diagram illustrating an exemplary system 200
in which concepts consistent with the invention may be implemented.
System 200 includes a computing device 201 that has a
computer-readable medium 209, such as random access memory, coupled
to a processor 208. Computing device 201 may also include a number
of additional external or internal devices, such as, without
limitation, a mouse, a CD-ROM, a keyboard, and a display.
[0023] In general, computing device 201 may be any type of
computing platform, and may be connected to a network 202.
Computing device 201 is exemplary only. Concepts consistent with
the present invention can be implemented on any computing device,
whether or not connected to a network.
[0024] Processor 208 can be any of a number of well-known computer
processors, such as processors from Intel Corporation, of Santa
Clara, Calif. Processor 208 executes program instructions stored in
memory 209.
[0025] Memory 209 contains an application program 215. In
particular, application program 215 may implement the thematic
segmentation tool described below. The thematic segmentation tool
215 may receive input data, such as linguistically segmented text,
from other application programs executing in computing device 201
or other computing devices, such as those connected to computing
device 201 through network 202. Thematic segmentation tool 215
processes the input data to generate indications of thematic
segments.
Thematic Segmentation Tool
[0026] FIG. 3 is a block diagram conceptually illustrating software
elements of thematic segmentation tool 215. Decisions relating to
thematic segmentation are made by thematic decision component 310.
Thematic decision component 310 implements a statistical framework
that generates thematic segments for a "document." The term
document, as used herein, refers to a textual information and
associated descriptive information relating to the document (e.g.,
speaker boundaries, phrasal boundaries, etc.). Although such a
document may be generated from data from audio sources, it could be
generated in other manners, such as from data from video or textual
sources.
[0027] Thematic decision component 310 receives a number of inputs
that describe the document. Specifically, as shown in FIG. 3,
thematic decision component 310 receives a text transcript of the
document from transcription component 320, speaker boundary
information from speaker boundary detection component 321,
linguistic information from linguistic detection component 322, and
topic classifications from topic classification component 323.
Although transcription component 320, speaker boundary detection
component 321, linguistic detection component 322, and topic
classification component 323 are illustrated as part of thematic
segmentation tool 215, in other implementations, these components
may be considered as providing input information to a thematic
segmentation tool implemented by thematic decision component
310.
[0028] Transcription component 320 may be an automated or manual
transcription tool that converts the audio input stream it receives
into text. Transcription component 320 may use conventional
techniques to perform the conversion.
[0029] Speaker boundary detection component 321 locates boundaries
between speakers in the audio input stream. Knowledge of speaker
changes in an audio stream may be a useful indicator of potential
changes in thematic content. Automated speaker boundary detection
techniques are known in the art. For example, speaker boundary
detection is described in Liu et al., "Fast Speaker Change
Detection for Broadcast News Transcription and Indexing,"
Eurospeech '99, Budapest, Hungary, September 99, pp. 1031-1034.;
and Chen et al., "Speaker, Environment, and Channel Change
Detection and Clustering via the Bayesian Information Criterion,"
Proceedings of the DARPA Broadcast News Transcription and
Understanding Workshop, Lansdowne, Va., 1998. Alternatively,
instead of automatically detecting speaker boundaries, the speaker
boundaries may be manually inserted into the document.
[0030] Linguistic detection component 322 receives the text
generated by transcription component 320 and the audio input
stream. Automated transcription techniques generally produce a
simple stream of words without linguistic information (e.g.,
periods, exclamation marks, quotation marks) that would ideally be
associated with the text. Linguistic component 322 annotates the
text from transcription component 320 to include this linguistic
information. In addition to visible linguistic information, such as
periods, linguistic component 322 may associate non-visible
linguistic information, such as phrasal boundaries, with the
received text.
[0031] Techniques for generating both visible and non-visible
linguistic information are described in detail in U.S. patent
application Ser. No. ______ (Attorney Docket Number 02-4024),
titled "Linguistic Segmentation of Speech," filed ______, the
contents of which are hereby incorporated by reference.
[0032] Topic classification component 323 generates topics selected
from a predefined topic vocabulary that are relevant to the
document. For example, a document may include any combination of
words from a 60,000 word corpus. Topic classification component 323
examines the document and outputs one or more predefined topics,
where the number of possible topics is less than the 60,000 word
corpus (e.g., a 5,000 word topic vocabulary).
[0033] Topic classification component 323, in one implementation,
uses a Bayesian framework to generate topics for a document. More
particularly, topic classification component 323 may be implemented
as a probabilistic Hidden Markov Model (HMM) whose parameters are
estimated from training samples of documents with given topic
labels. This model allows each word in a document to contribute
different amounts to each of the topics assigned to the document.
The output of topic classification component 323 may be a
rank-ordered list of all possible topics and corresponding scores
that indicate the estimated relevance of each topic. In general,
automated topic classification systems are known in the art. See,
for example, Makhoul et al., "Speech and Language Technologies for
Audio Indexing and Retrieval," Proceedings of the IEEE, vol. 88,
no. 8, August 2000.
[0034] In another possible implementation, instead of estimating
parameters based on training samples that have topics manually
generated, topic classification component 323 can be constructed to
generate topics based on unsupervised topic discovery.
[0035] Thematic decision component 310 uses the outputs of
transcription component 320, speaker boundary detection component
321, linguistic detection component 322, and topic classification
component 323 to generate indications of thematic segments in the
input document.
[0036] Consistent with an aspect of the present invention, the
thematic segments generated by thematic decision component 310 may
include multiple overlapping thematic segments for a particular
portion of a document. Additionally, thematic decision component
310 may label the thematic segments using a hierarchical labeling
scheme such that a specific thematic segment (e.g., a thematic
segment labeled "hurricane") is organized as a subset of a more
general thematic segment (e.g., the thematic segment labeled
"weather").
[0037] FIG. 4 is a diagram conceptually illustrating exemplary
thematic segments for a document. Document 401 is conceptually
illustrated as a series of lines that are assumed to correspond to
text. Associated with the text in document 401 are linguistic cues
such as periods 402 and commas 403. Although not shown, speaker
boundaries, topics, and non-visible linguistic information may also
be associated with document 401.
[0038] Thematic segments in FIG. 4 are illustrated by the bracketed
segments 410-412. As shown, thematic segment 410 and 412 overlap
one another. In general, thematic segments do not necessarily have
to sequentially follow one another. Thematic segment 412 may be
hierarchically related to thematic segment 410 as a subset of
thematic segment 410, or thematic segment 412 may be an independent
and concurrent thematic segment.
Detailed Description of Statistical Framework used by the Thematic
Segmentation Tool
[0039] In general, when generating thematic segments, such as
thematic segments 410-412, thematic decision component 310 honors
the linguistic boundary information as basic constituents of the
document. Thematic segments are formed as one or more of the
sequential constituents (e.g., one or more sentences) determined by
linguistic detection component 322.
[0040] A number of different techniques can be used to implement
the statistical framework of thematic decision component 310. Some
of these techniques, and the speech features on which they are
based, will now be described in more detail.
[0041] Acoustic Features
[0042] Speech has a range of properties that make it very different
from plain text. Thematic segmentation on speech-transcribed text
gains from the fact that the problem now has access to the original
signal from the speaker in addition to the textual content of what
was spoken. Nuances in speech from the speaker are frequently very
relevant indicators of changes in content as well as intent by the
speaker, both of which can be used to effectively model the shift
in themes in an episode. Prosodic features, such as pause, pitch,
energy, and, speaking rate, can be used in statistical models for
detecting changes in the speech that correspond to a change in the
theme of the content.
[0043] Linguistic Features
[0044] Word repetition can be used alone or in conjunction with
other features like word frequency and synonyms. In most cases,
synonyms are identified using predefined word-tables or word
thesaurus, both of which are hard-to-generate-and-generalize
resources. Latent Semantic Analysis (LSA) is a known robust
technique used to match words that are synonyms and better handle
the multiple meanings of a term. An example of the use of LSA is
given in T. Brants, "Topic-Based Document Segmentation with
Probabilistic Latent Semantic Analysis," Proceedings of the
Conference on Information and Knowledge Management, Nov. 4-9, 2002,
McLean, Va. LSA uses singular value decomposition to map the
high-dimensional word-document count matrix to a lower dimensional
latent `semantic` space wherein terms and documents that are
closely associated are placed near one another. LSA has the
additional property that it can reduce the dimensions of the
linguistic features space (typically of the order of 60,000 terms
for conventional large-vocabulary speech recognition systems) to
much more manageable size and do this intelligently such that the
inherent similarities between the terms in the space is not only
preserved, it is collated for better modeling. Additional
linguistic features, like Minimum Description Length (MDL) phrases,
and named-entity phrases, can be added to the linguistic sub-space
and rely on the LSA technique to connect the terms and the phrases
effectively.
[0045] Segmentation Approaches
[0046] Segmentation techniques can compare the distance between two
blocks of text and select segmentation points based on the
similarity values between pairs of adjacent blocks. For Example, M.
A. Hearst, "Multi-paragraph Segmentation of Expository Text,"
Proceedings of the Association for Computational Linguistics, 1994,
uses a sliding window and computes similarities between adjacent
blocks based on their term frequency vectors. For thematic
segmentation in speech, sliding windows of text can be used with a
similarity measure based on the persistence of
statistical-model-based hypothesized topics between pairs of
adjacent blocks of windowed-text. The smallest unit for the
segmentation process is an elementary block. Sentences can be used
as the elementary blocks for defining the segmentation candidates.
The text can be broken into blocks, i.e., sequences of consecutive
elementary blocks, where each block includes some number of
elementary blocks. In the training documents, these blocks are
variable-sized, non-overlapping and generally do not cross segment
boundaries. However, in the documents to be segmented, these blocks
may be overlapping, as in the use of a sliding window. The set of
positions between every pair of adjacent blocks compose the
segmentation candidates.
[0047] Mathematical Models
[0048] There are a number of mathematical models that can be used
to determine the relationship between varied features and shifts in
thematic content. The models can operate on the pure acoustic
features or the pure linguistic features, as well as the combined
acoustic/linguistic features. For example, statistical
learn-by-example techniques can be trained on roughly annotated
training data for every domain and language may be used.
[0049] Neural Networks
[0050] Neural Networks can additionally be effective in
approximating the complex, non-linear relationships that exist
between features of various types, such as continuous, discrete,
and in some cases even Boolean, as well as the change in structure
in the underlying speech. Neural Networks can be used to model the
acoustic features and produce an estimate of the similarity or
dissimilarity between adjacent blocks on either side of a
segmentation candidate. With the help of LSA, high-dimensional
linguistic features can be mapped onto a low dimensional compact
sub-space. The mapped features can be used with the prosodic
information in a combined neural network to detect changes in
themes.
[0051] Probabilistic Latent Semantic Analysis (PLSA)
[0052] Probabilistic Latent Semantic Analysis can be used to model
high-dimensional linguistic features. The PLSA model can be used
quite effectively with the combined feature space since it is
highly adept at finding the subtle cross-correlations between
features that expose the inter-relations between the terms and
underlying themes. PLSA is a statistical latent class model that
may provide better results than LSA for term matching in retrieval
applications. In PLSA, the conditional probability between
documents d and feature terms f is modeled through a latent
variable z, which can be loosely thought of as a class or topic. A
PLSA model is parameterized by P(f.vertline.z) and P(z.vertline.d),
and the words may belong to more than one class and a document may
discuss more than one "topic". The latent variable z can be thought
of as an unknown variable in the context of
Expectation-Maximization algorithms and thus the parameters of the
PLSA model can be trained from a corpus of documents using the EM
algorithm. The use of PLSA allows for a better representation of
sparse information in a text block, such as a sentence or a
sequence of sentences. A wide variety of similarity measures like
the cosine distance, the Bhattacharya distance, as well as
Kullback-Leibler divergence can be used with the scores generated
from the PLSA model to determine the segmentation boundaries.
CONCLUSION
[0053] As described above, a thematic segmentation tool demarcates
segments for a document that have similar thematic content. The
thematic segmentation tool bases the thematic segments on a
transcription of audio data augmented with additional information
relating to linguistic and speaker descriptive properties of the
audio. The thematic segments generated by the tool may be
hierarchical and may include multiple different thematic segments
for a portion of text.
[0054] The foregoing description of preferred embodiments of the
invention provides illustration and description, but is not
intended to be exhaustive or to limit the invention to the precise
form disclosed. Modifications and variations are possible in light
of the above teachings or may be acquired from practice of the
invention.
[0055] Certain portions of the invention have been described as
software that performs one or more functions. The software may more
generally be implemented as any type of logic. This logic may
include hardware, such as application specific integrated circuit
or a field programmable gate array, software, or a combination of
hardware and software.
[0056] No element, act, or instruction used in the description of
the present application should be construed as critical or
essential to the invention unless explicitly described as such.
Also, as used herein, the article "a" is intended to include one or
more items. Where only one item is intended, the term "one" or
similar language is used.
[0057] The scope of the invention is defined by the claims and
their equivalents.
* * * * *