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Seminar at Speech, Music and Hearing:

Licentiate seminar:

Accounting for individual speaker properties in automatic speech recognition

Daniel Elenius

Opponent: Torbjørn Svendsen, NTNU

Abstract

In this work, speaker characteristic modeling has been applied in the fields of automatic speech recognition (ASR) and automatic speaker verification (ASV). In ASR, a key problem is that acoustic mismatch between training and test conditions degrade classification performance. In this work, a child exemplifies a speaker not represented in training data and methods to reduce the spectral mismatch are devised and evaluated. To reduce the acoustic mismatch, predictive modeling based on spectral speech transformation is applied. Following this approach, a model suitable for a target speaker, not well represented in the training data, is estimated and synthesized by applying vocal tract predictive modeling (VTPM). In this thesis, the traditional static modeling on the utterance level is extended to dynamic modeling. This is accomplished by operating also on sub-utterance units, such as phonemes, phone-realizations, sub-phone realizations and sound frames.

Initial experiments shows that adaptation of an acoustic model trained on adult speech significantly reduced the word error rate of ASR for children, but not to the level of a model trained on children’s speech. Multi-speaker-group training provided an acoustic model that performed recognition for both adults and children within the same model at almost the same accuracy as speaker-group dedicated models, with no added model complexity. In the analysis of the cause of errors, body height of the child was shown to be correlated to word error rate.

A further result is that the computationally demanding iterative recognition process in standard VTLN can be replaced by synthetically extending the vocal tract length distribution in the training data. A multi-warp model is trained on the extended data and recognition is performed in a single pass. The accuracy is similar to that of the standard technique.

A concluding experiment in ASR shows that the word error rate can be reduced by extending a static vocal tract length compensation parameter into a temporal parameter track. A key component to reach this improvement was provided by a novel joint two-level optimization process. In the process, the track was determined as a composition of a static and a dynamic component, which were simultaneously optimized on the utterance and sub-utterance level respectively. This had the principal advantage of limiting the modulation amplitude of the track to what is realistic for an individual speaker. The recognition error rate was reduced by 10% relative compared with that of a standard utterance-specific estimation technique.

The techniques devised and evaluated can also be applied to other speaker characteristic properties, which exhibit a dynamic nature.

An excursion into ASV led to the proposal of a statistical speaker population model. The model represents an alternative approach for determining the reject/accept threshold in an ASV system instead of the commonly used direct estimation on a set of client and impostor utterances. This is especially valuable in applications where a low false reject or false accept rate is required. In these cases, the number of errors is often too few to estimate a reliable threshold using the direct method. The results are encouraging but need to be verified on a larger database.

15:15 - 17:00
Friday April 23, 2010

The seminar is held in Fantum.

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Last updated: Wednesday, 23-Jun-2010 09:22:46 MEST