Topic
Speaker recognition
About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.
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TL;DR: A scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker using formants and a formant vocoder is proposed.
207 citations
IBM1
TL;DR: The authors explore the trade-off between packing information into sequences of feature vectors and being able to model them accurately and investigate a method of parameter estimation which is designed to cope with inaccurate modeling assumptions.
Abstract: The acoustic-modelling problem in automatic speech recognition is examined from an information theoretic point of view. This problem is to design a speech-recognition system which can extract from the speech waveform as much information as possible about the corresponding word sequence. The information extraction process is factored into two steps: a signal-processing step which converts a speech waveform into a sequence of informative acoustic feature vectors, and a step which models such a sequence. The authors are primarily concerned with the use of hidden Markov models to model sequences of feature vectors which lie in a continuous space. They explore the trade-off between packing information into such sequences and being able to model them accurately. The difficulty of developing accurate models of continuous-parameter sequences is addressed by investigating a method of parameter estimation which is designed to cope with inaccurate modeling assumptions. >
207 citations
IBM1
TL;DR: In this paper, confusion coefficients between the labels of the label alphabet for initial training and those for adaptation are determined by alignment of adaption speech with the corresponding initially trained Markov model.
Abstract: For circumstance adaption, for example, speaker adaption, confusion coefficients between the labels of the label alphabet for initial training and those for adaption are determined by alignment of adaption speech with the corresponding initially trained Markov model. That is, each piece of adaptation speech is aligned with a corresponding initially trained Markov model by the Viterbi algorithm, and each label in the adaption speech is mapped onto one of the states of the Markov models. In respect of each adaptation lable ID, the parameter values for each initial training label of the states which are mapped onto the adaptation label in concern are accumulated and normalized to generate a confusion coefficient between each initial training label and each adaptation label. The parameter table of each Markov model is rewritten in respect of the adaptation label alphabet using the confusion coefficients.
204 citations
27 Aug 2007
TL;DR: In this article, a method that integrates the phase information on a speaker recognition method was proposed, which reduced the speaker recognition error rate by about 44% by using phase information for speaker identification.
Abstract: In conventional speaker recognition method based on MFCC, the phase information has been ignored. In this paper, we proposed a method that integrates the phase information on a speaker recognition method. The speaker identification experiments were performed using NTT database which consists of sentences uttered at normal speed mode by 35 Japanese speakers (22 males and 13 females) on five sessions over ten months. Each speaker uttered only 5 training utterances (about 20 seconds in total). Using the phaseinformation, the speaker recognition error rate was reduced by about 44%. Index Terms: speaker identification, MFCC, phase information, GMM, combination method
204 citations