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Dynamic behaviour of connectionist speech recognition with strong latency constraints
- Speech Comm
"... This paper describes the use of connectionist techniques in phonetic speech recognition with strong latency constraints. The constraints are imposed by the task of deriving the lip movements of a synthetic face in real time from the speech signal, by feeding the phonetic string into an articulatory ..."
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Cited by 8 (6 self)
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This paper describes the use of connectionist techniques in phonetic speech recognition with strong latency constraints. The constraints are imposed by the task of deriving the lip movements of a synthetic face in real time from the speech signal, by feeding the phonetic string into an articulatory synthesiser. Particular attention has been paid to analysing the interaction between the time evolution model learnt by the multi-layer perceptrons and the transition model imposed by the Viterbi decoder, in different latency conditions. Two experiments were conducted in which the time dependencies in the language model (LM) were controlled by a parameter. The results show a strong interaction between the three factors involved, namely the neural network topology, the length of time dependencies in the LM and the decoder latency. Key words: speech recognition, neural network, low latency, non-linear dynamics 1
Abberley The THISL SDR system at TREC-9
- Proceedings of TREC-9
, 2000
"... This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration o ..."
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Cited by 5 (0 self)
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This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration of the speech recognition and text retrieval systems, including segmentation and query expansion. We report our results for development tests using the TREC-8 queries, and for the TREC-9 evaluation. 1.
NEURAL NETWORKS FOR DISTANT SPEECH RECOGNITION
"... Distant conversational speech recognition is challenging ow-ing to the presence of multiple, overlapping talkers, additional non-speech acoustic sources, and the effects of reverberation. In this paper we review work on distant speech recognition, with an emphasis on approaches which combine multich ..."
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Distant conversational speech recognition is challenging ow-ing to the presence of multiple, overlapping talkers, additional non-speech acoustic sources, and the effects of reverberation. In this paper we review work on distant speech recognition, with an emphasis on approaches which combine multichan-nel signal processing with acoustic modelling, and investi-gate the use of hybrid neural network / hidden Markov model acoustic models for distant speech recognition of meetings recorded using microphone arrays. In particular we investi-gate the use of convolutional and fully-connected neural net-works with different activation functions (sigmoid, rectified linear, and maxout). We performed experiments on the AMI and ICSI meeting corpora, with results indicating that neu-ral network models are capable of significant improvements in accuracy compared with discriminatively trained Gaussian mixture models. Index Terms — convolutional neural networks, distant speech recognition, rectifier unit, maxout networks, beam-forming, meetings, AMI corpus, ICSI corpus 1.
Evaluation of extractive voicemail summarization
- In Proc. ISCA Workshop on Multilingual Spoken Document Retrieval, Hong Kong
, 2003
"... This paper is about the evaluation of a system that generates short text summaries of voicemail messages, suitable for transmission as text messages. Our approach to summarization is based on a speech-recognized transcript of the voicemail message, from which a set of summary words is extracted. The ..."
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Cited by 3 (1 self)
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This paper is about the evaluation of a system that generates short text summaries of voicemail messages, suitable for transmission as text messages. Our approach to summarization is based on a speech-recognized transcript of the voicemail message, from which a set of summary words is extracted. The system uses a classifier to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. Our evaluations of the system, using a slot error rate metric, have compared manual and automatic summarization, and manual and automatic recognition (using two different recognizers). We also report on two subjective evaluations using mean opinion score of summaries, and a set of comprehension tests. The main results from these experiments were that the perceived difference in quality of summarization was affected more by errors resulting from automatic transcription, than by the automatic summarization process. 1.
Robust Automatic Speech Recognition With Unreliable Data
, 1999
"... Theoretical and practical issues of some of the problems in robust automatic speech recognition (ASR) and some of the techniques that address them are presented in this report. The problem of the robustness of the ASR in real--life (as opposed to laboratory) conditions is paramount to the widespread ..."
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Theoretical and practical issues of some of the problems in robust automatic speech recognition (ASR) and some of the techniques that address them are presented in this report. The problem of the robustness of the ASR in real--life (as opposed to laboratory) conditions is paramount to the widespread deployment of speech enabled products. The report reviews techniques used so far for robust ASR, ranging from simple spectrum subtraction to various types of model adaptation. A possible connection of robust ASR with the computational auditory scene analysis (CASA), methods for local Signal--to--Noise Ratio (SNR) estimation and classification/scoring with on--line adapted statistical models is discussed. The main focus is on the techniques that would allow for incorporation of CASA and local SNR estimates (used as methods for speech/non--speech separation) into the present prevailing stochastic pattern matching paradigms -- Hidden Markov models (HMM) and artificial neural networks (ANN). Th...
Regularization of context-dependent deep neural networks with context-independent multi-task training
- in Proc. ICASSP
, 2015
"... The use of context-dependent targets has become standard in hybrid DNN systems for automatic speech recognition. However, we argue that despite the use of state-tying, optimising to context-dependent targets can lead to over-fitting, and that discriminating between ar-bitrary tied context-dependent ..."
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The use of context-dependent targets has become standard in hybrid DNN systems for automatic speech recognition. However, we argue that despite the use of state-tying, optimising to context-dependent targets can lead to over-fitting, and that discriminating between ar-bitrary tied context-dependent targets may not be optimal. We pro-pose a multitask learning method where the network jointly predicts context-dependent and monophone targets. We evaluate the method on a large-vocabulary lecture recognition task and show that it yields relative improvements of 3-10 % over baseline systems. Index Terms — deep neural networks, multitask learning, regu-larization 1.
Development, Evaluation and Automatic Segmentation of Slovenian Broadcast News Speech Database
"... The paper reviews the development of a new Slovenian broadcast news speech database. The database consists of audio, video and annotation transcripts of about 34 hours of television daily news program captured from the public TV station RTVSLO. The paper addresses issues concerning transcription and ..."
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The paper reviews the development of a new Slovenian broadcast news speech database. The database consists of audio, video and annotation transcripts of about 34 hours of television daily news program captured from the public TV station RTVSLO. The paper addresses issues concerning transcription and annotation of the collected data, provides information on content analysis and basic statistics of the collected material and compares different methods of automatic segmentation. 1.
Unsupervised Speech Processing . . . Query-by-Example Spoken Term Detection
, 2013
"... This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech d ..."
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This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms
