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

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

Using Speech Recognition Software to Increase Writing Fluency for Individuals with Physical Disabilities

Writing is an important skill that is necessary throughout school and life. Many students with physical disabilities, however, have difficulty with writing skills due to disability-specific factors, such as motor coordination problems. Due to the difficulties these individuals have with writing, assistive technology is often utilized. One piece of assistive technology, speech recognition software, may help remove the motor demand of writing and help students become more fluent writers. Past research on the use of speech recognition software, however, reveals little information regarding its impact on individuals with physical disabilities. Therefore, this study involved students of high school age with physical disabilities that affected hand use. Using an alternating treatments design to compare the use of word processing with the use of speech recognition software, this study analyzed first-draft writing samples in the areas of fluency, accuracy, type of word errors, recall of intended meaning, and length. Data on fluency, calculated in words correct per minute (wcpm) indicated that all participants wrote much faster with speech recognition compared to word processing. However, accuracy, calculated as percent correct, was much lower when participants used speech recognition compared to word processing. Word errors and recall of intended meaning were coded based on type and varied across participants. In terms of length, all participants wrote longer drafts when using speech recognition software, primarily because their fluency was higher, and they were able, therefore, to write more words. Although the results of this study indicated that participants wrote more fluently with speech recognition, because their accuracy was low, it is difficult to determine whether or not speech recognition is a viable solution for all individuals with physical disabilities. Therefore, additional research is needed that takes into consideration the editing and error correction time when using speech recognition software

Vocabulary size influences spontaneous speech in native language users: Validating the use of automatic speech recognition in individual differences research

Previous research has shown that vocabulary size affects performance on laboratory word production tasks. Individuals who know many words show faster lexical access and retrieve more words belonging to pre-specified categories than individuals who know fewer words. The present study examined the relationship between receptive vocabulary size and speaking skills as assessed in a natural sentence production task. We asked whether measures derived from spontaneous responses to every-day questions correlate with the size of participants’ vocabulary. Moreover, we assessed the suitability of automatic speech recognition for the analysis of participants’ responses in complex language production data. We found that vocabulary size predicted indices of spontaneous speech: Individuals with a larger vocabulary produced more words and had a higher speech-silence ratio compared to individuals with a smaller vocabulary. Importantly, these relationships were reliably identified using manual and automated transcription methods. Taken together, our results suggest that spontaneous speech elicitation is a useful method to investigate natural language production and that automatic speech recognition can alleviate the burden of labor-intensive speech transcription

Nonlinear Dynamic Invariants for Continuous Speech Recognition

In this work, nonlinear acoustic information is combined with traditional linear acoustic information in order to produce a noise-robust set of features for speech recognition. Classical acoustic modeling techniques for speech recognition have relied on a standard assumption of linear acoustics where signal processing is primarily performed in the signal\u27s frequency domain. While these conventional techniques have demonstrated good performance under controlled conditions, the performance of these systems suffers significant degradations when the acoustic data is contaminated with previously unseen noise. The objective of this thesis was to determine whether nonlinear dynamic invariants are able to boost speech recognition performance when combined with traditional acoustic features. Several sets of experiments are used to evaluate both clean and noisy speech data. The invariants resulted in a maximum relative increase of 11.1% for the clean evaluation set. However, an average relative decrease of 7.6% was observed for the noise-contaminated evaluation sets. The fact that recognition performance decreased with the use of dynamic invariants suggests that additional research is required for robust filtering of phase spaces constructed from noisy time series

Exploiting correlogram structure for robust speech recognition with multiple speech sources

This paper addresses the problem of separating and recognising speech in a monaural acoustic mixture with the presence of competing speech sources. The proposed system treats sound source separation and speech recognition as tightly coupled processes. In the first stage sound source separation is performed in the correlogram domain. For periodic sounds, the correlogram exhibits symmetric tree-like structures whose stems are located on the delay that corresponds to multiple pitch periods. These pitch-related structures are exploited in the study to group spectral components at each time frame. Local pitch estimates are then computed for each spectral group and are used to form simultaneous pitch tracks for temporal integration. These processes segregate a spectral representation of the acoustic mixture into several time-frequency regions such that the energy in each region is likely to have originated from a single periodic sound source. The identified time-frequency regions, together with the spectral representation, are employed by a `speech fragment decoder' which employs `missing data' techniques with clean speech models to simultaneously search for the acoustic evidence that best matches model sequences. The paper presents evaluations based on artificially mixed simultaneous speech utterances. A coherence-measuring experiment is first reported which quantifies the consistency of the identified fragments with a single source. The system is then evaluated in a speech recognition task and compared to a conventional fragment generation approach. Results show that the proposed system produces more coherent fragments over different conditions, which results in significantly better recognition accuracy

Sub-Sync: automatic synchronization of subtitles in the broadcasting of true live programs in spanish

Individuals With Sensory Impairment (Hearing Or Visual) Encounter Serious Communication Barriers Within Society And The World Around Them. These Barriers Hinder The Communication Process And Make Access To Information An Obstacle They Must Overcome On A Daily Basis. In This Context, One Of The Most Common Complaints Made By The Television (Tv) Users With Sensory Impairment Is The Lack Of Synchronism Between Audio And Subtitles In Some Types Of Programs. In Addition, Synchronization Remains One Of The Most Significant Factors In Audience Perception Of Quality In Live-Originated Tv Subtitles For The Deaf And Hard Of Hearing. This Paper Introduces The Sub-Sync Framework Intended For Use In Automatic Synchronization Of Audio-Visual Contents And Subtitles, Taking Advantage Of Current Well-Known Techniques Used In Symbol Sequences Alignment. In This Particular Case, These Symbol Sequences Are The Subtitles Produced By The Broadcaster Subtitling System And The Word Flow Generated By An Automatic Speech Recognizing The Procedure. The Goal Of Sub-Sync Is To Address The Lack Of Synchronism That Occurs In The Subtitles When Produced During The Broadcast Of Live Tv Programs Or Other Programs That Have Some Improvised Parts. Furthermore, It Also Aims To Resolve The Problematic Interphase Of Synchronized And Unsynchronized Parts Of Mixed Type Programs. In Addition, The Framework Is Able To Synchronize The Subtitles Even When They Do Not Correspond Literally To The Original Audio And/Or The Audio Cannot Be Completely Transcribed By An Automatic Process. Sub-Sync Has Been Successfully Tested In Different Live Broadcasts, Including Mixed Programs, In Which The Synchronized Parts (Recorded, Scripted) Are Interspersed With Desynchronized (Improvised) Ones