Use of both manually and automatically labelled data for model training is referred to as semi-supervised training. While semi-supervised acoustic model training has been well-explored in the context of hidden Markov Gaussian mixture models (HMM-GMMs), the re-emergence of deep neural network (DNN) acoustic models has given rise to some novel approaches to semi-supervised DNN training. This paper investigates several different strategies for semi-supervised DNN training, including the so-called `shared hidden layer' approach and the `knowledge distillation' (or student-teacher) approach. Particular attention is paid to the differing behaviour of semi-supervised DNN training methods during the cross-entropy and sequence training phases of model building. Experimental results on our internal study dataset provide evidence that in a low-resource scenario the most effective semi-supervised training strategy is `naive CE' (treating manually transcribed and automatically transcribed data identically during the cross entropy phase of training) followed by use of a shared hidden layer technique during sequence training.

The effectiveness of unsupervised speaker adaptation is typically limited by errors in the estimated transcription of the adaptation data. Previous work has mitigated this negative effect by using only those sections of the adaptation data which are transcribed with relatively high confidence. In this work, phoneme correctness predictions are integrated into a discriminative unsupervised speaker adaptation procedure. Significant accuracy improvements (over the equivalent likelihood-based technique) are observed when using discriminative unsupervised speaker adaptation in combination with support vector machines to predict phoneme correctness.

Unsupervised acoustic model adaptation for large vocabulary speech recognition is typically accomplished by using an estimated transcription of the adaptation data. The effectiveness of the technique is limited by errors in the estimated transcription. Previous work has mitigated this negative effect by using only those sections of the adaptation data which are transcribed with relatively high confidence. In this work, phoneme correctness predictions are integrated into a discriminative unsupervised acoustic model adaptation procedure. Small but significant performance improvements (over the equivalent maximum likelihood adaptation technique) are observed when using unsupervised discriminative adaptation in combination with support vector machines to predict phoneme correctness.

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

This paper demonstrates how unsupervised cross-lingual adaptation of HMM-based speech synthesis models may be performed without explicit knowledge of the adaptation data language. A two-pass decision tree construction technique is deployed for this purpose. Using parallel translated datasets, cross-lingual and intralingual adaptation are compared in a controlled manner. Listener evaluations reveal that the proposed method delivers performance approaching that of unsupervised intralingual adaptation.

Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to firstly estimate the transcription of the adaptation data. By defining a mapping between HMM-based synthesis models and ASR-style models, this paper introduces an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for supplementary acoustic models. Further, this enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data.

The minimum Bayes risk (MBR) criterion has previously been applied to the task of speaker adaptation in large vocabulary continuous speech recognition. The success of unsupervised MBR speaker adaptation, however, has been limited by the accuracy of the estimated transcription of the acoustic data. This paper addresses this issue not by improving the accuracy of the estimated transcription but via temporal masking of its erroneous regions. 1.

The Minimum Bayes Risk (MBR) framework has been a successful strategy for the training of hidden Markov models for large vocabulary speech recognition. Practical implementations of MBR must select an appropriate hypothesis space and loss function. The set of word sequences and a word-based Levenshtein distance may be assumed to be the optimal choice but use of phoneme-based criteria appears to be more successful. This paper compares the use of different hypothesis spaces and loss functions defined using the system constituents of word, phone, physical triphone, physical state and physical mixture component. For practical reasons the competing hypotheses are constrained by sampling. The impact of the sampling technique on the performance of MBR training is also examined. Index Terms: discriminative training, Minimum Bayes Risk. 1.