3,222 research outputs found

    Robust Classification of Stop Consonants Using Auditory-Based Speech Processing

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    In this work, a feature-based system for the automatic classification of stop consonants, in speaker independent continuous speech, is reported. The system uses a new auditory-based speech processing front-end that is based on the biologically rooted property of average localized synchrony detection (ALSD). It incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved, statistically, to be rich in their information content. The experiments are performed on stop consonants extracted from the TIMIT database with additive white Gaussian noise at various signal-to-noise ratios. The obtained classification accuracy compares favorably with previous work. The results also showed a consistent improvement of 3% in the place detection over the Generalized Synchrony Detector (GSD) system under identical circumstances on clean and noisy speech. This illustrates the superior ability of the ALSD to suppress the spurious peaks and produce a consistent and robust formant (peak) representation

    Articulatory features for robust visual speech recognition

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    Phonologically-Informed Speech Coding for Automatic Speech Recognition-based Foreign Language Pronunciation Training

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    Automatic speech recognition (ASR) and computer-assisted pronunciation training (CAPT) systems used in foreign-language educational contexts are often not developed with the specific task of second-language acquisition in mind. Systems that are built for this task are often excessively targeted to one native language (L1) or a single phonemic contrast and are therefore burdensome to train. Current algorithms have been shown to provide erroneous feedback to learners and show inconsistencies between human and computer perception. These discrepancies have thus far hindered more extensive application of ASR in educational systems. This thesis reviews the computational models of the human perception of American English vowels for use in an educational context; exploring and comparing two types of acoustic representation: a low-dimensionality linguistically-informed formant representation and more traditional Mel frequency cepstral coefficients (MFCCs). We first compare two algorithms for phoneme classification (support vector machines and long short-term memory recurrent neural networks) trained on American English vowel productions from the TIMIT corpus. We then conduct a perceptual study of non-native English vowel productions perceived by native American English speakers. We compare the results of the computational experiment and the human perception experiment to assess human/model agreement. Dissimilarities between human and model classification are explored. More phonologically-informed audio signal representations should create a more human-aligned, less L1-dependent vowel classification system with higher interpretability that can be further refined with more phonetic- and/or phonological-based research. Results show that linguistically-informed speech coding produces results that better align with human classification, supporting use of the proposed coding for ASR-based CAPT

    Exploring the connection of acoustic and distinctive features

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    This study is a contribution to link the abstract phonological level to the acoustic signal level by identifying the main acoustic correlates for the distinctive feature set developed by Chomsky and Halle (1968). The acoustic features were extracted by the openSMILE toolkit from spontaneous speech data. For each distinctive feature a set of closely related acoustic features was derived by means of correlation-based feature selection. Based on the respective acoustic feature pools C4.5 trees and support vector machines for binary feature classification were trained. The classification performance ranged from 76 to 89% for vocalic features and from 78 to 93% for consonantal features. The methods proposed in this study can be of use to identify systematic speech signal correspondencies for phonological models and as a starting point for distinctive feature detection in speech recognition

    Towards a Multimodal Silent Speech Interface for European Portuguese

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    Automatic Speech Recognition (ASR) in the presence of environmental noise is still a hard problem to tackle in speech science (Ng et al., 2000). Another problem well described in the literature is the one concerned with elderly speech production. Studies (Helfrich, 1979) have shown evidence of a slower speech rate, more breaks, more speech errors and a humbled volume of speech, when comparing elderly with teenagers or adults speech, on an acoustic level. This fact makes elderly speech hard to recognize, using currently available stochastic based ASR technology. To tackle these two problems in the context of ASR for HumanComputer Interaction, a novel Silent Speech Interface (SSI) in European Portuguese (EP) is envisioned.info:eu-repo/semantics/acceptedVersio

    Physiologically-Motivated Feature Extraction Methods for Speaker Recognition

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    Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new features based on vocal source characteristics intended to capture physiological information related to the laryngeal excitation energy of a speaker. These features, including RPCC, GLFCC and TPCC, represent the unique characteristics of speech production not represented in current state-of-the-art speaker identification systems. The proposed features are evaluated through three experimental paradigms including cross-lingual speaker identification, cross song-type avian speaker identification and mono-lingual speaker identification. The experimental results show that the proposed features provide information about speaker characteristics that is significantly different in nature from the phonetically-focused information present in traditional spectral features. The incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks

    ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•œ DNN ๊ธฐ๋ฐ˜ ์Œํ–ฅ ๋ชจ๋ธ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๊น€๋‚จ์ˆ˜.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•ด์„œ DNN์„ ํ™œ์šฉํ•œ ์Œํ–ฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” DNN์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์žก์Œ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ๊ฐ•์ธํ•จ์„ ๋ณด์กฐ ํŠน์ง• ๋ฒกํ„ฐ๋“ค์„ ํ†ตํ•˜์—ฌ ์ตœ๋Œ€๋กœ ํ™œ์šฉํ•˜๋Š” ์Œํ–ฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์ด๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ DNN์€ ์™œ๊ณก๋œ ์Œ์„ฑ, ๊นจ๋—ํ•œ ์Œ์„ฑ, ์žก์Œ ์ถ”์ •์น˜, ๊ทธ๋ฆฌ๊ณ  ์Œ์†Œ ํƒ€๊ฒŸ๊ณผ์˜ ๋ณต์žกํ•œ ๊ด€๊ณ„๋ฅผ ๋ณด๋‹ค ์›ํ™œํ•˜๊ฒŒ ํ•™์Šตํ•˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ๊ธฐ๋ฒ•์€ Aurora-5 DB ์—์„œ ๊ธฐ์กด์˜ ๋ณด์กฐ ์žก์Œ ํŠน์ง• ๋ฒกํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๋ชจ๋ธ ์ ์‘ ๊ธฐ๋ฒ•์ธ ์žก์Œ ์ธ์ง€ ํ•™์Šต (noise-aware training, NAT) ๊ธฐ๋ฒ•์„ ํฌ๊ฒŒ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” DNN์„ ํ™œ์šฉํ•œ ๋‹ค ์ฑ„๋„ ํŠน์ง• ํ–ฅ์ƒ ๊ธฐ๋ฒ•์ด๋‹ค. ๊ธฐ์กด์˜ ๋‹ค ์ฑ„๋„ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ์ „ํ†ต์ ์ธ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์ธ ๋น”ํฌ๋ฐ ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ ํ–ฅ์ƒ๋œ ๋‹จ์ผ ์†Œ์Šค ์Œ์„ฑ ์‹ ํ˜ธ๋ฅผ ์ถ”์ถœํ•˜๊ณ  ๊ทธ๋ฅผ ํ†ตํ•˜์—ฌ ์Œ์„ฑ์ธ์‹์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ธฐ์กด์˜ ๋น”ํฌ๋ฐ ์ค‘์—์„œ ๊ฐ€์žฅ ๊ธฐ๋ณธ์  ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ delay-and-sum (DS) ๋น”ํฌ๋ฐ ๊ธฐ๋ฒ•๊ณผ DNN์„ ๊ฒฐํ•ฉํ•œ ๋‹ค ์ฑ„๋„ ํŠน์ง• ํ–ฅ์ƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” DNN์€ ์ค‘๊ฐ„ ๋‹จ๊ณ„ ํŠน์ง• ๋ฒกํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๊ณต๋™ ํ•™์Šต ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์™œ๊ณก๋œ ๋‹ค ์ฑ„๋„ ์ž…๋ ฅ ์Œ์„ฑ ์‹ ํ˜ธ๋“ค๊ณผ ๊นจ๋—ํ•œ ์Œ์„ฑ ์‹ ํ˜ธ์™€์˜ ๊ด€๊ณ„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ multichannel wall street journal audio visual (MC-WSJAV) corpus์—์„œ์˜ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ, ๊ธฐ์กด์˜ ๋‹ค์ฑ„๋„ ํ–ฅ์ƒ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ถˆํ™•์ •์„ฑ ์ธ์ง€ ํ•™์Šต (Uncertainty-aware training, UAT) ๊ธฐ๋ฒ•์ด๋‹ค. ์œ„์—์„œ ์†Œ๊ฐœ๋œ ๊ธฐ๋ฒ•๋“ค์„ ํฌํ•จํ•˜์—ฌ ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•œ ๊ธฐ์กด์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•๋“ค์€ ๊ฐ๊ฐ์˜ ๋„คํŠธ์›Œํฌ์˜ ํƒ€๊ฒŸ์„ ์ถ”์ •ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ๊ฒฐ์ •๋ก ์ ์ธ ์ถ”์ • ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ด๋Š” ์ถ”์ •์น˜์˜ ๋ถˆํ™•์ •์„ฑ ๋ฌธ์ œ ํ˜น์€ ์‹ ๋ขฐ๋„ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ œ์•ˆํ•˜๋Š” UAT ๊ธฐ๋ฒ•์€ ํ™•๋ฅ ๋ก ์ ์ธ ๋ณ€ํ™” ์ถ”์ •์„ ํ•™์Šตํ•˜๊ณ  ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์ธ ๋ณ€ํ™” ์˜คํ† ์ธ์ฝ”๋” (variational autoencoder, VAE) ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. UAT๋Š” ์™œ๊ณก๋œ ์Œ์„ฑ ํŠน์ง• ๋ฒกํ„ฐ์™€ ์Œ์†Œ ํƒ€๊ฒŸ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๋งค๊ฐœํ•˜๋Š” ๊ฐ•์ธํ•œ ์€๋‹‰ ๋ณ€์ˆ˜๋ฅผ ๊นจ๋—ํ•œ ์Œ์„ฑ ํŠน์ง• ๋ฒกํ„ฐ ์ถ”์ •์น˜์˜ ๋ถ„ํฌ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ธ๋งํ•œ๋‹ค. UAT์˜ ์€๋‹‰ ๋ณ€์ˆ˜๋“ค์€ ๋”ฅ ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์Œํ–ฅ ๋ชจ๋ธ์— ์ตœ์ ํ™”๋œ uncertainty decoding (UD) ํ”„๋ ˆ์ž„์›Œํฌ๋กœ๋ถ€ํ„ฐ ์œ ๋„๋œ ์ตœ๋Œ€ ์šฐ๋„ ๊ธฐ์ค€์— ๋”ฐ๋ผ์„œ ํ•™์Šต๋œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ Aurora-4 DB์™€ CHiME-4 DB์—์„œ ๊ธฐ์กด์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•๋“ค์„ ํฌ๊ฒŒ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.In this thesis, we propose three acoustic modeling techniques for robust automatic speech recognition (ASR). Firstly, we propose a DNN-based acoustic modeling technique which makes the best use of the inherent noise-robustness of DNN is proposed. By applying this technique, the DNN can automatically learn the complicated relationship among the noisy, clean speech and noise estimate to phonetic target smoothly. The proposed method outperformed noise-aware training (NAT), i.e., the conventional auxiliary-feature-based model adaptation technique in Aurora-5 DB. The second method is multi-channel feature enhancement technique. In the general multi-channel speech recognition scenario, the enhanced single speech signal source is extracted from the multiple inputs using beamforming, i.e., the conventional signal-processing-based technique and the speech recognition process is performed by feeding that source into the acoustic model. We propose the multi-channel feature enhancement DNN algorithm by properly combining the delay-and-sum (DS) beamformer, which is one of the conventional beamforming techniques and DNN. Through the experiments using multichannel wall street journal audio visual (MC-WSJ-AV) corpus, it has been shown that the proposed method outperformed the conventional multi-channel feature enhancement techniques. Finally, uncertainty-aware training (UAT) technique is proposed. The most of the existing DNN-based techniques including the techniques introduced above, aim to optimize the point estimates of the targets (e.g., clean features, and acoustic model parameters). This tampers with the reliability of the estimates. In order to overcome this issue, UAT employs a modified structure of variational autoencoder (VAE), a neural network model which learns and performs stochastic variational inference (VIF). UAT models the robust latent variables which intervene the mapping between the noisy observed features and the phonetic target using the distributive information of the clean feature estimates. The proposed technique outperforms the conventional DNN-based techniques on Aurora-4 and CHiME-4 databases.Abstract i Contents iv List of Figures ix List of Tables xiii 1 Introduction 1 2 Background 9 2.1 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Experimental Database . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Aurora-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2 Aurora-5 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 MC-WSJ-AV DB . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 CHiME-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3 Two-stage Noise-aware Training for Environment-robust Speech Recognition 25 iii 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Noise-aware Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Two-stage NAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Upper DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 Joint Training . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.1 GMM-HMM System . . . . . . . . . . . . . . . . . . . . . . . 37 3.4.2 Training and Structures of DNN-based Techniques . . . . . . 37 3.4.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 40 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4 DNN-based Feature Enhancement for Robust Multichannel Speech Recognition 45 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2 Observation Model in Multi-Channel Reverberant Noisy Environment 49 4.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3.2 Upper DNN and Joint Training . . . . . . . . . . . . . . . . . 54 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4.1 Recognition System and Feature Extraction . . . . . . . . . . 56 4.4.2 Training and Structures of DNN-based Techniques . . . . . . 58 4.4.3 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 62 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 iv 5 Uncertainty-aware Training for DNN-HMM System using Varia- tional Inference 67 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.2 Uncertainty Decoding for Noise Robustness . . . . . . . . . . . . . . 72 5.3 Variational Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.4 VIF-based uncertainty-aware Training . . . . . . . . . . . . . . . . . 83 5.4.1 Clean Uncertainty Network . . . . . . . . . . . . . . . . . . . 91 5.4.2 Environment Uncertainty Network . . . . . . . . . . . . . . . 93 5.4.3 Prediction Network and Joint Training . . . . . . . . . . . . . 95 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.5.1 Experimental Setup: Feature Extraction and ASR System . . 96 5.5.2 Network Structures . . . . . . . . . . . . . . . . . . . . . . . . 98 5.5.3 Eects of CUN on the Noise Robustness . . . . . . . . . . . . 104 5.5.4 Uncertainty Representation in Dierent SNR Condition . . . 105 5.5.5 Result of Speech Recognition . . . . . . . . . . . . . . . . . . 112 5.5.6 Result of Speech Recognition with LSTM-HMM . . . . . . . 114 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6 Conclusions 127 Bibliography 131 ์š”์•ฝ 145Docto
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