844 results about "Automatic speech" patented technology

An iterative process is provided for interacting with a voice information service. Such a service may permit, for example, a user to search one or more databases and may provide one or more search results to the user. Such a service may be suitable, for example, for searching for a desired entity or object within the database(s) using speech as an input and navigational tool. Applications of such a service may include, for instance, speech-enabled searching services such as a directory assistance service or any other service or application involving a search of information. In one example implementation, an automatic speech recognition (ASR) system is provided that performs a speech recognition and database search in an iterative fashion. With each iteration, feedback may be provided to the user presenting potentially relevant results. In one specific ASR system, a user desiring to locate information relating to a particular entity or object provides an utterance to the ASR. Upon receiving the utterance, the ASR determines a recognition set of potentially relevant search results related to the utterance and presents to the user recognition set information in an interface of the ASR. The recognition set information includes, for instance, reference information stored internally at the ASR for a plurality of potentially relevant recognition results. The recognition set information may be used as input to the ASR providing a feedback mechanism. In one example implementation, the recognition set information may be used to determine a restricted grammar for performing a further recognition.

A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.

An automatic speech recognition system includes: a sound source localization module for localizing a sound direction of a speaker based on the acoustic signals detected by the plurality of microphones; a sound source separation module for separating a speech signal of the speaker from the acoustic signals according to the sound direction; an acoustic model memory which stores direction-dependent acoustic models that are adjusted to a plurality of directions at intervals; an acoustic model composition module which composes an acoustic model adjusted to the sound direction, which is localized by the sound source localization module, based on the direction-dependent acoustic models, the acoustic model composition module storing the acoustic model in the acoustic model memory; and a speech recognition module which recognizes the features extracted by a feature extractor as character information using the acoustic model composed by the acoustic model composition module.

Ordering recognition results produced by an automatic speech recognition (‘ASR’) engine for a multimodal application implemented with a grammar of the multimodal application in the ASR engine, with the multimodal application operating in a multimodal browser on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to the ASR engine through a VoiceXML interpreter, includes: receiving, in the VoiceXML interpreter from the multimodal application, a voice utterance; determining, by the VoiceXML interpreter using the ASR engine, a plurality of recognition results in dependence upon the voice utterance and the grammar; determining, by the VoiceXML interpreter according to semantic interpretation scripts of the grammar, a weight for each recognition result; and sorting, by the VoiceXML interpreter, the plurality of recognition results in dependence upon the weight for each recognition result.

A method including detecting a selection of a plurality of erroneous words in text presented on a display of a device, in an automatic speech recognition system, receiving sequentially dictated corrections for the selected erroneous words in a single, continuous operation where each dictated correction corresponds to at least one of the selected erroneous words, and replacing the plurality of erroneous words with one or more corresponding words of the dictated corrections where each erroneous word is matched with the one or more corresponding words of the dictated corrections in an order the erroneous words appear according to a reading direction of the text.

A system, method, and computer readable medium storing a software program for translating a script for an interactive voice response system to a script for a visual interactive response system. The visual interactive response system executes the translated visual-based script when a user using a display telephone calls the visual interactive response system. The visual interactive response system then transmits a visual menu to the display telephone to allow the user to select a desired response, which is subsequently sent back to the visual interactive response system for processing. The voice-based script may be defined in voice extensible markup language and the visual-based script may be defined in wireless markup language, hypertext markup language, or handheld device markup language. The translation system and program includes a parser for extracting command structures from the voice-based script, a visual-based structure generator for generating corresponding command structure for the visual-based script, a text prompt combiner for incorporating text translated from voice prompts into command structure generated by the structure generator, an automatic speech recognition routine for automatically converting voice prompts into translated text, and an editor for editing said visual-based script.

A system and method for rescoring the N-best hypotheses from an automatic speech recognition system by comparing an original speech waveform to synthetic speech waveforms that are generated for each text sequence of the N-best hypotheses. A distance is calculated from the original speech waveform to each of the synthesized waveforms, and the text associated with the synthesized waveform that is determined to be closest to the original waveform is selected as the final hypothesis. The original waveform and each synthesized waveform are aligned to a corresponding text sequence on a phoneme level. The mean of the feature vectors which align to each phoneme is computed for the original waveform as well as for each of the synthesized hypotheses. The distance of a synthesized hypothesis to the original speech signal is then computed as the sum over all phonemes in the hypothesis of the Euclidean distance between the means of the feature vectors of the frames aligning to that phoneme for the original and the synthesized signals. The text of the hypothesis which is closest under the above metric to the original waveform is chosen as the final system output.

A speech recognition method includes the steps of receiving input speech containing vocabulary, processing the input speech with a grammar to obtain N-best hypotheses and associated parameter values, and determining whether a first-best hypothesis of the N-best hypotheses is confusable with any vocabulary within the grammar. The first-best hypothesis is accepted as recognized speech corresponding to the received input speech if the first-best hypothesis is not determined to be confusable with any vocabulary within the grammar. Where the first-best hypothesis is determined to be confusable, at least one parameter value of the first-best hypothesis can be compared to at least one threshold value. The first-best hypothesis can be accepted as recognized speech corresponding to the received input speech, if the parameter value of the first-best hypothesis is greater than the threshold value.

A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.

A mobile terminal (100) which is controllable by spoken utterances like proper names or command words is described. The mobile terminal (100) comprises an interface (200) for receiving from a network server (300) acoustic models for automatic speech recognition and an automatic speech recognizer (110) for recognizing the spoken utterances based on the received acoustic models. The invention further relates to a network server (300) for mobile terminals (100) which are controllable by spoken utterances and to a method for obtaining acoustic models for a mobile terminal (100) controllable by spoken utterances.

A system and method of testing and tuning a speech recognition system by providing pronunciations to the speech recognizer. First a text document is provided to the system and converted into a sequence of phonemes representative of the words in the text. The phonemes are then converted to model units, such as Hidden Markov Models. From the models a probability is obtained for each model or state, and feature vectors are determined. The feature vector matching the most probable vector for each state is selected for each model. These ideal feature vectors are provided to the speech recognizer, and processed. The end result is compared with the original text, and modifications to the system can be made based on the output text.

Exemplary embodiments of methods and apparatuses for automatic speech recognition are described. First model parameters associated with a first representation of an input signal are generated. The first representation of the input signal is a discrete parameter representation. Second model parameters associated with a second representation of the input signal are generated. The second representation of the input signal includes a continuous parameter representation of residuals of the input signal. The first representation of the input signal includes discrete parameters representing first portions of the input signal. The second representation includes discrete parameters representing second portions of the input signal that are smaller than the first portions. Third model parameters are generated to couple the first representation of the input signal with the second representation of the input signal. The first representation and the second representation of the input signal are mapped into a vector space.

Speech recognition is performed in near-real-time and improved by exploiting events and event sequences, employing machine learning techniques including boosted classifiers, ensembles, detectors and cascades and using perceptual clusters. Speech recognition is also improved using tandem processing. An automatic punctuator injects punctuation into recognized text streams.

Presenting supplemental content for digital media using a multimodal application, implemented with a grammar of the multimodal application in an automatic speech recognition (‘ASR’) engine, with the multimodal application operating on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to the ASR engine, includes: rendering, by the multimodal application, a portion of the digital media; receiving, by the multimodal application, a voice utterance from a user; determining, by the multimodal application using the ASR engine, a recognition result in dependence upon the voice utterance and the grammar; identifying, by the multimodal application, supplemental content for the rendered portion of the digital media in dependence upon the recognition result; and rendering, by the multimodal application, the supplemental content.

A system and method of operating an automatic speech recognition service using a client-server architecture is used to make automatic speech recognition (ASR) and text to speech (TTS) services accessible at a client location remote from the location of the main ASR and TTS engines. The present invention utilizes client-server communications over a packet network, such as the Internet or a wireless network, where the ASR/TTS server receives a grammar from the client or selects from a locally second plurality of grammars, receives information representing speech from the client, performs speech recognition, and returns information based upon the recognized speech to the client.

A method is disclosed for the detection of acronyms and digits and for finding the pronunciations for them. The method can be incorporated as part of an Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) system. Moreover, the method can be part of Multi-Lingual Automatic Speech Recognition (ML-ASR) and TTS systems. The method of handling of acronyms in a speech recognition and text-to-speech system can include detecting an acronym from text, identifying a language of the text based on non-acronym words in the text, and utilizing the identified language in acronym pronunciation generation to generate a pronunciation for the detected acronym.

A language recognition system, method and program product for recognizing language based input from computer users on a network of connected computers. Each computer includes at least one user based language model trained for a corresponding user for automatic speech recognition, handwriting recognition, machine translation, gesture recognition or other similar actions that require interpretation of user activities. Network computer users are clustered into classes of similar users according to user similarities such as, nationality, profession, sex, age, etc. User characteristics are collected by sensors and from databases and, then, distributed over the network during user activities. Language models with similarities among similar users on the network are identified. The language models include a language model domain, with similar language models being clustered according to their domains. Language models identified as similar are modified in response to user production activities. After modification of one language model, other identified similar language models are compared and adapted. Also, user data, including information about user activities and language model data, is transmitted over the network to other similar users. Language models are adapted only in response to similar user activities, when these activities are recorded and transmitted over the network. Language models are given a global context based on similar users that are connected together over the network.