59 results about "Speaker adaptation" patented technology

A method and a system for speech recognition are provided. In the method, vocal characteristics are captured from speech data and used to identify a speaker identification of the speech data. Next, a first acoustic model is used to recognize a speech in the speech data. According to the recognized speech and the speech data, a confidence score of the speech recognition is calculated and it is determined whether the confidence score is over a threshold. If the confidence score is over the threshold, the recognized speech and the speech data are collected, and the collected speech data is used for performing a speaker adaptation on a second acoustic model corresponding to the speaker identification.

A computer-implemented method is described for speaker adaptation in automatic speech recognition. Speech recognition data from a particular speaker is used for adaptation of an initial speech recognition acoustic model to produce a speaker adapted acoustic model. A speaker dependent differential acoustic model is determined that represents differences between the initial speech recognition acoustic model and the speaker adapted acoustic model. In addition, an approach is also disclosed to estimate speaker-specific feature or model transforms over multiple sessions. This is achieved by updating the previously estimated transform using only adaptation statistics of the current session.

At the time of the speaker adaptation, first feature vector generation sections (7, 8, 9) generate a feature vector series [Ci, M] from which the additive noise and multiplicative noise are removed. A second feature vector generation section (12) generates a feature vector series [Si, M] including the features of the additive noise and multiplicative noise. A path search section (10) conducts a path search by comparing the feature vector series [Ci, m] to the standard vector [an, m] of the standard voice HMM (300). When the speaker adaptation section (11) conducts correlation operation on an average feature vector [S^n, m] of the standard vector [an, m] corresponding to the path search result Dv and the feature vector series [Si, m], the adaptive vector [xn, m] is generated. The adaptive vector [xn, m] updates the feature vector of the speaker adaptive acoustic model (400) used for the speech recognition.

Voice recognition methods and systems are disclosed. A voice signal is obtained for an utterance of a speaker. A runtime pitch is determined from the voice signal for the utterance. The speaker is categorized based on the runtime pitch and one or more acoustic model parameters are adjusted based on a categorization of the speaker. The parameter adjustment may be performed at any instance of time during the recognition. A voice recognition analysis of the utterance is then performed based on the acoustic model.

A method and apparatus for discriminative estimation of parameters in a maximum a posteriori (MAP) speaker adaptation condition, and a voice recognition apparatus having the apparatus and a voice recognition method using the method are provided. The method for discriminative estimation of parameters in a maximum a posteriori (MAP) speaker adaptation condition, in which at least speaker-independent model parameters and prior density parameters, which are standards in recognizing a speaker's voice, are obtained as the result of model training after fetching training sets on a plurality of speakers from a training database, has the steps of (a) classifying adaptation data among training sets for respective speakers; (b) obtaining model parameters adapted from adaptation data on each speaker by using the initial values of the parameters; (c) searching a plurality of candidate hypotheses on each uttered sentence of training sets by using the adapted model parameters, and calculating gradients of speaker-independent model parameters by measuring the degree of errors on each training sentence; and (d) when training sets of all speakers are adapted, updating parameters, which were set at the initial stage, based on the calculated gradients.

The invention relates to a DNN (Deep Neural Network)-HMM (Hidden Markov Model)-based civil aviation radiotelephony communication acoustic model construction method. The method includes the following steps that: a Chinese radiotelephony communication corpus is set up; civil aviation radiotelephony communication speech signals are pre-processed; Fbank features are extracted from the civil aviation radiotelephony communication speech signals and are adopted as civil aviation radiotelephony communication speech features; linear discrimination analysis, feature space maximum likelihood regression transformation and speaker adaptive training transformation processing are performed on the civil aviation radiotelephony communication speech features; and the processed speech features are utilized to build a DNN-HMM-based radiotelephony communication acoustic model. With the method of the invention adopted, the FBANK and MFCC features of radiotelephony communication speech are extracted to traina DNN network, so that the DNN-HMM acoustic model suitable for radiotelephony communication speech recognition can be obtained; and since a dictionary and a language model are combined, so that the feature enhanced DNN-HMM model can reduce the phoneme recognition error rate of the radiotelephony communication speech to 5.62% on the basis of constructed data.

A speech recognition method and apparatus perform speaker clustering and speaker adaptation using average model variation information over speakers while analyzing the quantity variation amount and the directional variation amount. In the speaker clustering method, a speaker group model variation is generated based on the model variation between a speaker-independent model and a training speaker ML model. In the speaker adaptation method, the model in which the model variation between a test speaker ML model and a speaker group ML model to which the test speaker belongs which is most similar to a training speaker group model variation is found, and speaker adaptation is performed on the found model. Herein, the model variation in the speaker clustering and the speaker adaptation are calculated while analyzing both the quantity variation amount and the directional variation amount. The present invention may be applied to any speaker adaptation algorithm of MLLR and MAP.

A speech recognition apparatus and method for real-time speaker adaptation are provided. The speech recognition apparatus may estimate a pitch of a speech section from an inputted speech signal, extract a speech feature for speech recognition based on the estimated pitch, and perform speech recognition with respect to the speech signal based on the speech feature. The speech recognition apparatus may be adaptively normalized depending on a speaker. Thus, the speech recognition apparatus may extract a speech feature for speech recognition, and may improve a performance of speech recognition based on the extracted speech feature.

A method for providing a context specific speaker adaptation user interface is disclosed. The method allows a specific user of a voice activated system to train a voice recognition system to understand specific uttered commands by receiving a training sequence initiation input, sending at least one command to be trained, receiving a selection input, prompting a verbal input, recording the verbal input, and linking a voice input with an instruction corresponding with the command input.

A method for generating a test-speaker-specific adaptive system for recognising sounds in speech spoken by a test speaker; the method employing:(i) training data comprising speech items spoken by the test speaker; and(ii) an input network component and a speaker adaptive output network, the input network component and speaker adaptive output network having been trained using training data from training speakers;the method comprising:(a) using the training data to train a test-speaker-specific adaptive model component of an adaptive model comprising the input network component, and the test-speaker-specific adaptive model component, and(b) providing the test-speaker-specific adaptive system comprising the input network component, the trained test-speaker-specific adaptive model component, and the speaker-adaptive output network.

A method includes providing a deep neural network acoustic model, receiving audio data including one or more utterances of a speaker, extracting a plurality of speech recognition features from the one or more utterances of the speaker, creating a speaker identity vector for the speaker based on the extracted speech recognition features, and adapting the deep neural network acoustic model for automatic speech recognition using the extracted speech recognition features and the speaker identity vector.