83 research outputs found
Phoneme-Grapheme Based Speech Recognition System
State-of-the-art Automatic Speech Recognition (ASR) systems typically use phoneme as the subword units. In this paper, we investigate a system where the word models are defined in-terms of two different subword units, i.e., phonemes and graphemes. We train models for both the subword units, and then perform decoding using either both or just one subword unit. We have studied this system for American English language where there is weak correspondence between the grapheme and phoneme. The results from our studies show that there is good potential in using grapheme as auxiliary subword units
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-character unidirectional recurrent neural network (RNN), which is
end-to-end trained with connectionist temporal classification (CTC), and an
RNN-based character-level language model (LM). The output values of the
CTC-trained RNN are character-level probabilities, which are processed by beam
search decoding. The RNN LM augments the decoding by providing long-term
dependency information. We propose tree-based online beam search with
additional depth-pruning, which enables the system to process infinitely long
input speech with low latency. This system not only responds quickly on speech
but also can dictate out-of-vocabulary (OOV) words according to pronunciation.
The proposed model achieves the word error rate (WER) of 8.90% on the Wall
Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284
training set.Comment: To appear in ICASSP 201
Advances in All-Neural Speech Recognition
This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we have found useful in training these networks. We evaluate our system
on the commonly used NIST 2000 conversational telephony test set, and
significantly exceed the previously published performance of similar systems,
both with and without the use of an external language model and decoding
technology
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
In conventional speech recognition, phoneme-based models outperform
grapheme-based models for non-phonetic languages such as English. The
performance gap between the two typically reduces as the amount of training
data is increased. In this work, we examine the impact of the choice of
modeling unit for attention-based encoder-decoder models. We conduct
experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various
target units (phoneme, grapheme, and word-piece); across all tasks, we find
that grapheme or word-piece models consistently outperform phoneme-based
models, even though they are evaluated without a lexicon or an external
language model. We also investigate model complementarity: we find that we can
improve WERs by up to 9% relative by rescoring N-best lists generated from a
strong word-piece based baseline with either the phoneme or the grapheme model.
Rescoring an N-best list generated by the phonemic system, however, provides
limited improvements. Further analysis shows that the word-piece-based models
produce more diverse N-best hypotheses, and thus lower oracle WERs, than
phonemic models.Comment: To appear in the proceedings of INTERSPEECH 201
Acoustic Modelling for Under-Resourced Languages
Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones.
In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages
A study of phoneme and grapheme based context-dependent ASR systems
In this paper we present a study of automatic speech recognition systems using context-dependent phonemes and graphemes as sub-word units based on the conventional HMM/GMM system as well as tandem system. Experimental studies conducted on three different continuous speech recognition tasks show that systems using only context-dependent graphemes can yield competitive performance on small to medium vocabulary tasks when compared to a context-dependent phoneme-based automatic speech recognition system. In particular, we demonstrate the utility of tandem features that use an MLP trained to estimate phoneme posterior probabilities in improving grapheme based recognition system performance by incorporating phonemic knowledge into the system without having to explicitly define a phonetically transcribed lexicon
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