Fritz Class

Fast speaker adaptation for speech recognition systems

Different speaker adaptation methods for speech recognition systems adapting automatically to new and unknown speakers in a short training phase are discussed. The adaptation techniques aim at transformations of feature vectors, optimized with respect to some constraints. Two different adaptation strategies are discussed. The first one is based on least mean-squared-error optimization. The second method is a codebook-driven feature transformation. Both adaptation techniques are incorporated into two different recognition systems: dynamic time warping (DTW) and hidden Markov modeling (HMM). The results show that in both systems speaker-adaptive error rates are close to speaker-dependent error rates. In the best case the mean error rate of four test speakers decreases by a factor of six compared to the interspeaker error rate without adaptation. A hardware realization of the speaker-adaptive HMM-recognizer is described.<<ETX>>

Fast speaker adaptation combined with soft vector quantization in an HMM speech recognition system

The authors describe a method for combining speaker adaptation by feature vector transformation with semi-continuous hidden Markov modeling (SCHMM). Since the reference speakers voice is represented in the SCHMM system by multidimensional Gaussian distributions, it is these distributions rather than feature vectors that must be transformed. The performance of hard-decision vector quantization (HVQ), soft-decision VQ (SVQ), and SCHMM are compared as are the speaker-adaptive and speaker-independent systems. In addition, the influence of dynamic features is investigated. The definition of subword units is optimized, and, with respect to full or diagonal covariance matrices and codebook size, the SCHMM system is optimized. Model initialization and distribution reestimation during training is introduced. Significant improvements are obtained compared to previously reported systems based on HVQ: from 71.6% to 84.6% (speaker-independent) and from 80.4% to 87.4% (speaker-adaptive) mean recognition rate under difficult conditions.<<ETX>>

Soft-decision vector quantization based on the Dempster/Shafer theory

The authors describe an algorithm for soft-decision vector quantization (SVQ) implemented in the acoustic front-end of a large-vocabulary speech recognizer based on discrete density HMMs (hidden Markov models) of small phonetic units. In contrast to hard-decision vector quantization (HVQ), the proposed approach transforms a feature vector into a number of symbols associated with credibility values computed according to statistical models of distances and evidential reasoning. SVQ is related to semi-continuous density HMMs (SCHMMs). In contrast to SCHMM, which is based on multidimensional, class-specific distributions of feature vectors, SVQ is based on one-dimensional distributions of distances and is therefore much simpler. Credibilities and associated symbols form the inputs to both the HMM-training and the recognition modules of the system. SVQ improves recognition results remarkably.<<ETX>>

Optimierung eines HMM-Spracherkennungssystems

Dieser Beitrag beschreibt mehrere Optimierungsschritte eines auf Hidden-Markov-Modellen basierenden Spracherkenners. Im einzelnen betrifft dies: Wortuntereinheiten, dynamische Merkmale, Vektorquantisierung sowie Grose und Art der verwendeten Co-debucher. Auserdem wird im Detail auf ein Verfahren zur schnellen Sprecheradaption eingegangen. Wir beschreiben dabei die Kombination von „Sprecheradaption durch Merkmalstransformation“ mit semi-kontinuierlichen Hidden-Markov-Modellen SCHMM [1, 5, 9, 10]. Da in einem solchen Erkennungssystem die Sprache eines Referenzsprechers nicht explizit in Form von Merkmals Vektoren, sondern nur in Form mehrdimensionaler Normalverteilungen vorliegt, mussen diese Verteilungen an Stelle der Merkmalsvektoren transformiert werden.

Eine schnelle Sprecheradaption für verschiedenartige Spracherkenungssyteme

Dieser Beitrag beschreibt ein Sprecheradaptionsverfahren, das es ermoglicht, neue und unbekannte Sprecher automatisch in einer kurzen Trainingsphase an ein voradaptiertes Erkennungssystem zu adaptieren. Das Adaptionsverfahren beruht auf einer Transformation der Merkmalsvektoren und wird optimiert mit Hilfe des minimalen mittleren quadratischen Fehler — Kriteriums (MQF) unter Berucksichtigung zusatzlicher Nebenbedingungen. Die Experimente an zwei verschiedenartigen Erkennungssystemen (HMM und DTW — Erkenner) sowie mit unterschiedlichen Merkmalen (spektralen und cepstralen) zeigen, das das Verfahren sowohl in unterschiedlichen Erkennungssystemen als auch bei verschiedenen Merkmalssatzen ohne Modifikation eingesetzt werden kann. Es last sich damit nahezu die sprecherabhangige Fehlerrate erreichen.