Thomas Hain

Vocal Tract Length Normalisation (VTLN) is a commonly used technique to normalise for inter-speaker variability. It is based on the speaker-specific warping of the frequency axis, param- eterised by a scalar warp factor. This factor is typically esti- mated using maximum likelihood. We discuss how VTLN may be applied to multiparty conversations, reporting a substantial decrease in word error rate in experiments using the ICSI meet- ings corpus. We investigate the behaviour of the VTLN warping factor and show that a stable estimate is not obtained. Instead it appears to be influenced by the context of the meeting, in par- ticular the current conversational partner. These results are con- sistent with predictions made by the psycholinguistic interactive alignment account of dialogue, when applied at the acoustic and phonological levels.

ABSTRACT This paper presents a new approach for rapid adaptation in the presence of highly diverse scenarios that takes advantage of information describing the input signals. We introduce a new method for joint factorisation of the background and the speaker in an eigenspace MLLR framework: Joint Factor Eigenspace MLLR (JFEMLLR). We further propose to use contextual information describing the speaker and background, such as tags or more complex metadata, to provide an immediate estimation of the best MLLR transformation for the utterance. This provides instant adaptation, since it does not require any transcription from a previous decoding stage. Evaluation in a highly diverse Automatic Speech Recognition (ASR) task, a modified version of WSJCAM0, yields an improvement of 26.9% over the baseline, which is an extra 1.2% reduction over two-pass MLLR adaptation.

ABSTRACT Large corpora of transcribed speech are rare and expensive to acquire, but valuable for ASR systems. Of current research interest are corpora of natural speech, i.e. far-field recordings of multiple speakers in noisy environments. In the big data era there are many speech transcriptions collected for purposes other than ASR, which omit features required by typical ASR systems such as timing information. If we could recover training data from such 'found' corpora this would open up large new resources for ASR research. We present a case study for this type of data recovery - becoming known as 'lightly supervised learning' - for a highly damaged corpus called Family Life. We use a novel comparison of a parallel decode and forced audio alignment to iteratively select and grow good data. Family Life also has unusual data mislabelling problems which can be addressed by an integrated tfidf approach. These methods reduce WER on the corpus from 83.0 to 57.2. We also discuss a probabilistic loose string alignment approach which removes untranscribed 'icebreaker' speech.

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