A Mouth Full of Words: Visually Consistent Acoustic Redubbing

This paper introduces a method for automatic redubbing of video that exploits the many-to-many mapping of phoneme sequences to lip movements modelled as dynamic visemes [1]. For a given utterance, the corresponding dynamic viseme sequence is sampled to construct a graph of possible phoneme sequences that synchronize with the video. When composed with a pronunciation dictionary and language model, this produces a vast number of word sequences that are in sync with the original video, literally putting plausible words into the mouth of the speaker. We demonstrate that traditional, one-to-many, static visemes lack flexibility for this application as they produce significantly fewer word sequences. This work explores the natural ambiguity in visual speech and offers insight for automatic speech recognition and the importance of language modeling

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Rapid Language Model Development Using External Resources for New Spoken Dialog Domains

This paper addresses a critical problem in deploying a spoken dialog system (SDS). One of the main bottlenecks of SDS deployment for a new domain is data sparseness in building a statistical language model. Our goal is to devise a method to efficiently build a reliable language model for a new SDS. We consider the worst yet quite common scenario where only a small amount (∼1.7K utterances) of domain specific data is available for the target domain. We present a new method that exploits external static text resources that are collected for other speech recognition tasks as well as dynamic text resources acquired from World Wide Web (WWW). We show that language models built using external resources can jointly be used with limited in–domain (baseline) language model to obtain significant improvements in speech recognition accuracy. Combining language models built using external resources with the in–domain language model provides over 20 % reduction in WER over the baseline in–domain language model. Equivalently, we achieve almost the same level of performance by having ten times as much in–domain data (17K utterances)

Recognition of Dialogue Acts in Multiparty Meetings using a Switching DBN

This paper is concerned with the automatic recognition of dialogue acts (DAs) in multiparty conversational speech. We present a joint generative model for DA recognition in which segmentation and classification of DAs are carried out in parallel. Our approach to DA recognition is based on a switching dynamic Bayesian network (DBN) architecture. This generative approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. The switching DBN coordinates the recognition process by integrating the component models. The factored language model, which is estimated from multiple conversational data corpora, is used in conjunction with additional task-specific language models. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. We have carried out experiments on the AMI corpus of multimodal meeting recordings, using both manually transcribed speech, and the output of an automatic speech recognizer, and using different configurations of the generative model. Our results indicate that the system performs well both on reference and fully automatic transcriptions. A further significant improvement in recognition accuracy is obtained by the application of the discriminative reranking approach based on conditional random fields

An HCI Speech-Based Architecture for Man-To-Machine and Machine-To-Man Communication in Yorùbá Language

Man communicates with man by natural language, sign language, and/or gesture but communicates with machine via electromechanical devices such as mouse, and keyboard.  These media of effecting Man-To-Machine (M2M) communication are electromechanical in nature. Recent research works, however, have been able to achieve some high level of success in M2M using natural language, sign language, and/or gesture under constrained conditions. However, machine communication with man, in reverse direction, using natural language is still at its infancy. Machine communicates with man usually in textual form. In order to achieve acceptable quality of end-to-end M2M communication, there is need for robust architecture to develop a novel speech-to-text and text-to-speech system. In this paper, an HCI speech-based architecture for Man-To-Machine and Machine-To-Man communication in Yorùbá language is proposed to carry Yorùbá people along in the advancement taking place in the world of Information Technology. Dynamic Time Warp is specified in the model to measure the similarity between the voice utterances in the sound library. In addition, Vector Quantization, Guassian Mixture Model and Hidden Markov Model are incorporated in the proposed architecture for compression and observation. This approach will yield a robust Speech-To-Text and Text-To-Speech system. Keywords: Yorùbá Language, Speech Recognition, Text-To-Speech, Man-To-Machine, Machine-To-Ma

Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model

Neural network pruning offers an effective method for compressing a multilingual automatic speech recognition (ASR) model with minimal performance loss. However, it entails several rounds of pruning and re-training needed to be run for each language. In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways). Our approach dynamically adapts the sub-network, avoiding premature decisions about a fixed sub-network structure. We show that our approach outperforms existing pruning methods when targeting sparse monolingual models. Further, we illustrate that Dynamic ASR Pathways jointly discovers and trains better sub-networks (pathways) of a single multilingual model by adapting from different sub-network initializations, thereby reducing the need for language-specific pruning

CTC-based Compression for Direct Speech Translation

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations. In this work, we propose the first method able to perform a dynamic compression of the input indirect ST models. In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics. Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.Comment: Accepted at EACL202

How to handle gender and number agreement in statistical language models?

The agreement in gender and number is a critical problem in statistical language modeling. One of the main difficulties in speech recognition of French language is the presence of misrecognized words due to the bad agreement (in gender and number) between words. Statistical language models do not treat this phenomena directly. This paper focuses on how to handle the issue of this agreement. We introduce an original model called Features-Cache (FC) to estimate the gender and the number of the word to predict. It is a dynamic variable-length Features-Cache. The size of the cache is automatically determined in accordance to syntagm delimitors. The main advantage of this model is that there is no need to any syntactic parsing: it is used as any other statistical language model. Several models have been carried out and the best one achieves an improvement of approximatively 9 points in terms of perplexity. This model has been integrated in a speech recognition system based on JULIUS engine. Tests have been carried out on 280 sentences provided by AUPELF for the French automatic speech recognition evaluation campaign. This new model outperforms the baseline one, in terms of word error, by 3%

How to handle gender and number agreement in statistical language models

Abstract The agreement in gender and number is a critical problem in statistical language modeling. One of the main difficulties in speech recognition of French language is the presence of misrecognized words due to the bad agreement (in gender and number) between words. Statistical language models do not treat this phenomena directly. This paper focuses on how to handle the issue of this agreement. We introduce an original model called Features-Cache (FC) to estimate the gender and the number of the word to predict. It is a dynamic variable-length Features-Cache. The size of the cache is automatically determined in accordance to syntagm delimitors. The main advantage of this model is that there is no need to any syntactic parsing : it is used as any other statistical language model. Several models have been carried out and the best one achieves an improvement of approximatively 9 points in terms of perplexity. This model has been integrated in a speech recognition system based on JULIUS engine. Tests have been carried out on 280 sentences provided by AUPELF for the French automatic speech recognition evaluation campaign. This new model outperforms the baseline one, in terms of word error, by 3%