IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.
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2001 IEEE Workshop on Automatic Speech Recognition and Understanding. ASRU 2001. Conference Proceedings (Cat. No.01EX544)
- Trang i - 2001
Presents the front cover of the proceedings record.
Continuous multi-band speech recognition using Bayesian networks
- Trang 41-44
Using the Bayesian networks framework, we present a new multi-band approach for continuous speech recognition. This new approach has the advantage of overcoming all the limitations of the standard multi-band techniques. Moreover, it leads to a higher fidelity speech modeling than HMMs. We provide a preliminary evaluation of the performance of our new approach on a connected digits recognition task...... hiện toàn bộ
#Speech recognition #Bayesian methods #Hidden Markov models #Random variables #Automatic speech recognition #Decoding #Vocabulary
Maximum-likelihood training of the PLCG-based language model
- Trang 210-213
In Van Uytsel et al. (2001) a parsing language model based on a probabilistic left-comer grammar (PLCG) was proposed and encouraging performance on a speech recognition task using the PLCG-based language model was reported. In this paper we show how the PLCG-based language model can be further optimized by iterative parameter reestimation on unannotated training data. The precalculation of forward...... hiện toàn bộ
#Natural languages #Speech recognition #Maximum likelihood estimation #Training data #Testing #Computer networks #Iterative algorithms #Large-scale systems #Stochastic processes #Predictive models
Searching for the missing piece [speech recognition]
- Trang 230-233
The tree-trellis forward-backward algorithm has been widely used for N-best searching in continuous speech recognition. In conventional approaches, the heuristic score used for the A* backward search is derived from the partial-path scores recorded during the forward pass. The inherently delayed use of a language model in the lexical tree structure leads to inefficient pruning and the partial-path...... hiện toàn bộ
#Lattices #Delay estimation #Speech #Tree data structures #Viterbi algorithm
Incremental language models for speech recognition using finite-state transducers
- Trang 194-197
In the context of the weighted finite-state transducer approach to speech recognition, we investigate a novel decoding strategy to deal with very large n-gram language models often used in large-vocabulary systems. In particular, we present an alternative to full, static expansion and optimization of the finite-state transducer network. This alternative is useful when the individual knowledge sour...... hiện toàn bộ
#Natural languages #Speech recognition #Decoding #Hidden Markov models #Acoustic transducers #Laboratories #Context modeling #Oceans #Surveillance #Oxygen
n-gram and decision tree based language identification for written words
- Trang 335-338
As the demand for multilingual speech recognizers increases, the development of systems which combine automatic language identification, language-specific pronunciation modeling and language-independent acoustic models becomes increasingly important. When the recognition grammar is dynamic and obtained directly from written text, the language associated with each grammar item has to be identified ...... hiện toàn bộ
#Decision trees #Natural languages #Speech recognition #Mobile handsets #Automatic speech recognition #Testing #Vocabulary #Usability #Signal processing #Embedded computing
Finite-state transducers for speech-input translation
- Trang 375-380
Nowadays, hidden Markov models (HMMs) and n-grams are the basic components of the most successful speech recognition systems. In such systems, HMMs (the acoustic models) are integrated into a n-gram or a stochastic finite-state grammar (the language model). Similar models can be used for speech translation, and HMMs (the acoustic models) can be integrated into a finite-state transducer (the transl...... hiện toàn bộ
#Hidden Markov models #Acoustic transducers #Stochastic processes #Speech recognition #Stochastic systems #Telephony #Prototypes #Search engines #Natural languages #Decoding
Adaptive training for robust ASR
- Trang 15-20
Adaptive training is a powerful training technique for building speech recognition systems on nonhomogeneous data. The aim is to remove unwanted variability, such as changes in speaker, channel or acoustic environment, from desired changes, the acoustic differences between words. During training, two sets of models are generated: a canonical model set for the desired "true" variability of the spee...... hiện toàn bộ
#Robustness #Automatic speech recognition #Loudspeakers #Speech recognition #Training data #Target recognition #Feature extraction #Acoustical engineering #Data engineering #Power engineering and energy
Recognition of negative emotions from the speech signal
- Trang 240-243
This paper reports on methods for automatic classification of spoken utterances based on the emotional state of the speaker. The data set used for the analysis comes from a corpus of human-machine dialogues recorded from a commercial application deployed by SpeechWorks. Linear discriminant classification with Gaussian class-conditional probability distribution and k-nearest neighbors methods are u...... hiện toàn bộ
#Emotion recognition #Speech recognition #Principal component analysis #Automatic speech recognition #Speech analysis #Man machine systems #Linear discriminant analysis #Probability distribution #Statistical distributions #Frequency
ASR in portable wireless devices
- Trang 96-102
This paper discusses the applicability and role of automatic speech recognition in portable wireless devices. Due to the author's background, the viewpoints are somewhat biased to mobile telephones, but many of the aspects are nevertheless common for other portable devices as well. While still dominated by the speaker-dependent technology, there are today signs that also in wireless devices, there...... hiện toàn bộ
#Automatic speech recognition #Costs #Embedded system #Acoustic noise #Noise robustness #Telephony #Mobile communication #Speech recognition #Acoustic devices #Adaptation model