Abstract: A user edits text in a draft document by providing input including left and right “anchor” text and replacement text. In response, a document editing system identifies an instance of the left anchor text followed by the right anchor text in the draft document, and replaces text between these instances with the replacement text specified by the user. For example, the user may type a string containing the left anchor text followed by the replacement text followed by the right anchor text, in response to which the system may perform the replacement just described. As a result, the user may specify both the location of, and a correction for, text in the draft document without using cursor keys or other navigation commands to navigate to the location of the text to be corrected, thereby increasing correction efficiency by avoiding the delay associated with such manual navigation.

Abstract: Non-verbalized tokens, such as punctuation, are automatically predicted and inserted into a transcription of speech in which the tokens were not explicitly verbalized. Token prediction may be integrated with speech decoding, rather than performed as a post-process to speech decoding.

Abstract: A user edits text in a draft document by providing input including left and right “anchor” text and replacement text. In response, a document editing system identifies an instance of the left anchor text followed by the right anchor text in the draft document, and replaces text between these instances with the replacement text specified by the user. For example, the user may type a string containing the left anchor text followed by the replacement text followed by the right anchor text, in response to which the system may perform the replacement just described. As a result, the user may specify both the location of, and a correction for, text in the draft document without using cursor keys or other navigation commands to navigate to the location of the text to be corrected, thereby increasing correction efficiency by avoiding the delay associated with such manual navigation.

Abstract: Non-verbalized tokens, such as punctuation, are automatically predicted and inserted into a transcription of speech in which the tokens were not explicitly verbalized. Token prediction may be integrated with speech decoding, rather than performed as a post-process to speech decoding.

Abstract: Techniques are disclosed for facilitating the process of proofreading draft transcripts of spoken audio streams. In general, proofreading of a draft transcript is facilitated by playing back the corresponding spoken audio stream with an emphasis on those regions in the audio stream that are highly relevant or likely to have been transcribed incorrectly. Regions may be emphasized by, for example, playing them back more slowly than regions that are of low relevance and likely to have been transcribed correctly. Emphasizing those regions of the audio stream that are most important to transcribe correctly and those regions that are most likely to have been transcribed incorrectly increases the likelihood that the proofreader will accurately correct any errors in those regions, thereby improving the overall accuracy of the transcript.

Abstract: Speech is transcribed to produce a draft transcript of the speech. Portions of the transcript having a high priority are identified. For example, particular sections of the transcript may be identified as high-priority sections. As another example, portions of the transcript requiring human verification may be identified as high-priority sections. High-priority portions of the transcript are verified at a first time, without verifying other portions of the transcript. Such other portions may or may not be verified at a later time. Limiting verification, either initially or entirely, to high-priority portions of the transcript limits the time required to perform such verification, thereby making it feasible to verify the most important portions of the transcript at an early stage without introducing an undue delay into the transcription process. Verifying the other portions of the transcript later ensures that early verification of the high-priority portions does not sacrifice overall verification accuracy.

Abstract: Speech is transcribed to produce a draft transcript of the speech. Portions of the transcript having a high priority are identified. For example, particular sections of the transcript may be identified as high-priority sections. As another example, portions of the transcript requiring human verification may be identified as high-priority sections. High-priority portions of the transcript are verified at a first time, without verifying other portions of the transcript. Such other portions may or may not be verified at a later time. Limiting verification, either initially or entirely, to high-priority portions of the transcript limits the time required to perform such verification, thereby making it feasible to verify the most important portions of the transcript at an early stage without introducing an undue delay into the transcription process. Verifying the other portions of the transcript later ensures that early verification of the high-priority portions does not sacrifice overall verification accuracy.

Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model using discriminative training techniques, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.

Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system my identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.

Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model using discriminative training techniques, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.

Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.

Abstract: Facts are extracted from speech and recorded in a document using codings. Each coding represents an extracted fact and includes a code and a datum. The code may represent a type of the extracted fact and the datum may represent a value of the extracted fact. The datum in a coding is rendered based on a specified feature of the coding. For example, the datum may be rendered as boldface text to indicate that the coding has been designated as an “allergy.” In this way, the specified feature of the coding (e.g., “allergy”-ness) is used to modify the manner in which the datum is rendered. A user inspects the rendering and provides, based on the rendering, an indication of whether the coding was accurately designated as having the specified feature. A record of the user's indication may be stored, such as within the coding itself.

Abstract: A speech recognition client sends a speech stream and control stream in parallel to a server-side speech recognizer over a network. The network may be an unreliable, low-latency network. The server-side speech recognizer recognizes a first portion of the speech stream and, if a predetermined criterion is satisfied by the speech recognition result, waits until the speech recognizer has been reconfigured before recognizing a second portion of the speech stream. The speech recognition client receives recognition results from the server-side recognizer in response to requests from the client. The client may remotely reconfigure the state of the server-side recognizer during recognition.

Abstract: A hybrid speech recognition system uses a client-side speech recognition engine and a server-side speech recognition engine to produce speech recognition results for the same speech. An arbitration engine produces speech recognition output based on one or both of the client-side and server-side speech recognition results.

Abstract: An error detection and correction system extracts editing patterns and derives correction rules from them by observing differences between draft documents and corresponding edited documents, and/or by observing editing operations performed on the draft documents to produce the edited documents. The system develops classifiers that partition the space of all possible contexts into equivalence classes and assigns one or more correction rules to each such class). Once the system has been trained, it may be used to detect and (optionally) correct errors in new draft documents. When presented with a draft document, the system identifies first content (e.g., text) in the draft document and identifies a context of the first content. The system identifies a correction rule based on the first content and the first context. The system may use a classifier to identify the correction rule. The system applies the correction rule to the first content to produce second content.

Abstract: Techniques are disclosed for automatically de-identifying spoken audio signals. In particular, techniques are disclosed for automatically removing personally identifying information from spoken audio signals and replacing such information with non-personally identifying information. De-identification of a spoken audio signal may be performed by automatically generating a report based on the spoken audio signal. The report may include concept content (e.g., text) corresponding to one or more concepts represented by the spoken audio signal. The report may also include timestamps indicating temporal positions of speech in the spoken audio signal that corresponds to the concept content. Concept content that represents personally identifying information is identified. Audio corresponding to the personally identifying concept content is removed from the spoken audio signal. The removed audio may be replaced with non-personally identifying audio.