3,157 research outputs found
E2E SPEECH RECOGNITION WITH CTC AND LOCAL ATTENTION
Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87%
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
Spoken language processing requires speech and natural language integration.
Moreover, spoken Korean calls for unique processing methodology due to its
linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic
spoken Korean processing engine, which emphasizes that: 1) connectionist and
symbolic techniques must be selectively applied according to their relative
strength and weakness, and 2) the linguistic characteristics of Korean must be
fully considered for phoneme recognition, speech and language integration, and
morphological/syntactic processing. The design and implementation of SKOPE
demonstrates how connectionist/symbolic hybrid architectures can be constructed
for spoken agglutinative language processing. Also SKOPE presents many novel
ideas for speech and language processing. The phoneme recognition,
morphological analysis, and syntactic analysis experiments show that SKOPE is a
viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be
presented at IJCAI95 workshops on new approaches to learning for natural
language processin
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
Chart-driven Connectionist Categorial Parsing of Spoken Korean
While most of the speech and natural language systems which were developed
for English and other Indo-European languages neglect the morphological
processing and integrate speech and natural language at the word level, for the
agglutinative languages such as Korean and Japanese, the morphological
processing plays a major role in the language processing since these languages
have very complex morphological phenomena and relatively simple syntactic
functionality. Obviously degenerated morphological processing limits the usable
vocabulary size for the system and word-level dictionary results in exponential
explosion in the number of dictionary entries. For the agglutinative languages,
we need sub-word level integration which leaves rooms for general morphological
processing. In this paper, we developed a phoneme-level integration model of
speech and linguistic processings through general morphological analysis for
agglutinative languages and a efficient parsing scheme for that integration.
Korean is modeled lexically based on the categorial grammar formalism with
unordered argument and suppressed category extensions, and chart-driven
connectionist parsing method is introduced.Comment: 6 pages, Postscript file, Proceedings of ICCPOL'9
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
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