%0 Journal Article %1 Digalakis1995 %A Digalakis, Vassilios V. %A Rtischev, Dimitry %A Neumeyer, Leonardo G. %D 1995 %J IEEE Transactions on Speech and Audio Processing %K English;Gaussian Gaussian Journal Markov Street Wall adaptation;Automatic analysis;Hidden corpus;performance;robust data;Vocabulary densities;constrained distribution;Robustness;Speech estimate;speaker estimation;Probability estimation;continuous hidden likelihood maximum-likelihood mixture-density mixtures;automatic models;Maximum models;error models;speech processes;error rate;large-vocabulary recognition recognition;American recognition;Degradation;Error recognition;Training speech statistics;hidden systems;component %N 5 %P 357-366 %R 10.1109/89.466659 %T Speaker adaptation using constrained estimation of Gaussian mixtures %V 3 %X A trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMMs). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training population. A popular technique that can improve the performance and robustness of a speech recognition system is adapting speech models to the speaker, and more generally to the channel and the task. In continuous mixture-density HMMs the number of component densities is typically very large, and it may not be feasible to acquire a sufficient amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, the authors propose a constrained estimation technique for Gaussian mixture densities. The algorithm is evaluated on the large-vocabulary Wall Street Journal corpus for both native and nonnative speakers of American English. For nonnative speakers, the recognition error rate is approximately halved with only a small amount of adaptation data, and it approaches the speaker-independent accuracy achieved for native speakers. For native speakers, the recognition performance after adaptation improves to the accuracy of speaker-dependent systems that use six times as much training data.

@article{Digalakis1995, abstract = {A trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMMs). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training population. A popular technique that can improve the performance and robustness of a speech recognition system is adapting speech models to the speaker, and more generally to the channel and the task. In continuous mixture-density HMMs the number of component densities is typically very large, and it may not be feasible to acquire a sufficient amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, the authors propose a constrained estimation technique for Gaussian mixture densities. The algorithm is evaluated on the large-vocabulary Wall Street Journal corpus for both native and nonnative speakers of American English. For nonnative speakers, the recognition error rate is approximately halved with only a small amount of adaptation data, and it approaches the speaker-independent accuracy achieved for native speakers. For native speakers, the recognition performance after adaptation improves to the accuracy of speaker-dependent systems that use six times as much training data.}, added-at = {2021-02-01T10:51:23.000+0100}, author = {Digalakis, Vassilios V. and Rtischev, Dimitry and Neumeyer, Leonardo G.}, biburl = {https://www.bibsonomy.org/bibtex/2231cdba1bf101bab7dc99320002dd259/m-toman}, doi = {10.1109/89.466659}, file = {:pdfs/digalakis_ieeetrans_1995.pdf:PDF}, interhash = {4dcea259ea50142d0f990b22368f4bc4}, intrahash = {231cdba1bf101bab7dc99320002dd259}, issn = {1063-6676}, journal = {IEEE Transactions on Speech and Audio Processing}, keywords = {English;Gaussian Gaussian Journal Markov Street Wall adaptation;Automatic analysis;Hidden corpus;performance;robust data;Vocabulary densities;constrained distribution;Robustness;Speech estimate;speaker estimation;Probability estimation;continuous hidden likelihood maximum-likelihood mixture-density mixtures;automatic models;Maximum models;error models;speech processes;error rate;large-vocabulary recognition recognition;American recognition;Degradation;Error recognition;Training speech statistics;hidden systems;component}, month = sep, number = 5, owner = {schabus}, pages = {357-366}, timestamp = {2021-02-01T10:51:23.000+0100}, title = {Speaker adaptation using constrained estimation of Gaussian mixtures}, volume = 3, year = 1995 }