3,256 research outputs found

    Production and perception of speaker-specific phonetic detail at word boundaries

    Get PDF
    Experiments show that learning about familiar voices affects speech processing in many tasks. However, most studies focus on isolated phonemes or words and do not explore which phonetic properties are learned about or retained in memory. This work investigated inter-speaker phonetic variation involving word boundaries, and its perceptual consequences. A production experiment found significant variation in the extent to which speakers used a number of acoustic properties to distinguish junctural minimal pairs e.g. 'So he diced them'โ€”'So he'd iced them'. A perception experiment then tested intelligibility in noise of the junctural minimal pairs before and after familiarisation with a particular voice. Subjects who heard the same voice during testing as during the familiarisation period showed significantly more improvement in identification of words and syllable constituents around word boundaries than those who heard different voices. These data support the view that perceptual learning about the particular pronunciations associated with individual speakers helps listeners to identify syllabic structure and the location of word boundaries

    The Resonant Dynamics of Speech Perception: Interword Integration and Duration-Dependent Backward Effects

    Full text link
    How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? During fluent speech perception, variations in the durations of speech sounds and silent pauses can produce different pereeived groupings. For exarnple, increasing the silence interval between the words "gray chip" may result in the percept "great chip", whereas increasing the duration of fricative noise in "chip" may alter the percept to "great ship" (Repp et al., 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In AHTWORD, sequential activation and storage of phonemic items in working memory provides bottom-up input to unitized representations, or list chunks, that group together sequences of items of variable length. The list chunks compete with each other as they dynamically integrate this bottom-up information. The winning groupings feed back to provide top-down supportto their phonemic items. Feedback establishes a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept. Because the resonance evolves more slowly than wotking memory activation, it can be influenced by information presented after relatively long intervening silence intervals. The same phonemic input can hereby yield different groupings depending on its arrival time. Processes of resonant transfer and competitive teaming help determine which groupings win the competition. Habituating levels of neurotransmitter along the pathways that sustain the resonant feedback lead to a resonant collapsee that permits the formation of subsequent. resonances.Air Force Office of Scientific Research (F49620-92-J-0225); Defense Advanced Research projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-92-J-1309, NOOO14-95-1-0657

    Spoken content retrieval: A survey of techniques and technologies

    Get PDF
    Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

    THE RELATIONSHIP BETWEEN ACOUSTIC FEATURES OF SECOND LANGUAGE SPEECH AND LISTENER EVALUATION OF SPEECH QUALITY

    Get PDF
    Second language (L2) speech is typically less fluent than native speech, and differs from it phonetically. While the speech of some L2 English speakers seems to be easily understood by native listeners despite the presence of a foreign accent, other L2 speech seems to be more demanding, such that listeners must expend considerable effort in order to understand it. One reason for this increased difficulty may simply be the speakerโ€™s pronunciation accuracy or phonetic intelligibility. If a L2 speakerโ€™s pronunciations of English sounds differ sufficiently from the sounds that native listeners expect, these differences may force native listeners to work much harder to understand the divergent speech patterns. However, L2 speakers also tend to differ from native ones in terms of fluency โ€“ the degree to which a speaker is able to produce appropriately structured phrases without unnecessary pauses, self-corrections or restarts. Previous studies have shown that measures of fluency are strongly predictive of listenersโ€™ subjective ratings of the acceptability of L2 speech: Less fluent speech is consistently considered less acceptable (Ginther, Dimova, & Yang, 2010). However, since less fluent speakers tend also to have less accurate pronunciations, it is unclear whether or how these factors might interact to influence the amount of effort listeners exert to understand L2 speech, nor is it clear how listening effort might relate to perceived quality or acceptability of speech. In this dissertation, two experiments were designed to investigate these questions

    Systems, interactions and macrotheory

    Get PDF
    A significant proportion of early HCI research was guided by one very clear vision: that the existing theory base in psychology and cognitive science could be developed to yield engineering tools for use in the interdisciplinary context of HCI design. While interface technologies and heuristic methods for behavioral evaluation have rapidly advanced in both capability and breadth of application, progress toward deeper theory has been modest, and some now believe it to be unnecessary. A case is presented for developing new forms of theory, based around generic โ€œsystems of interactors.โ€ An overlapping, layered structure of macro- and microtheories could then serve an explanatory role, and could also bind together contributions from the different disciplines. Novel routes to formalizing and applying such theories provide a host of interesting and tractable problems for future basic research in HCI

    Phonetics of segmental FO and machine recognition of Korean speech

    Get PDF

    Prosodic Phrasing in Spontaneous Swedish

    Get PDF
    One of the most important functions of prosody is to divide the flow of speech into chunks. The chunking, or prosodic phrasing, of speech plays an important role in both the production and perception of speech. This study represents a move away from the laboratory speech examined in previous, related studies on prosodic phrasing in Swedish, since a spontaneous, Southern Swedish speech material is investigated. The study is, however, not primarily intended as a study of the Southern Swedish dialect; rather Southern Swedish is used as a convenient object on which to test various hypotheses about the phrasing function of prosody in spontaneous speech. The study comprises both analyses of production data and perception experiments, and both the phonetics and phonology of prosodic phrasing is dealt with. First, the distribution of prosodic phrase boundaries in spontaneous speech is examined by considering it as a reflection of optimality theoretic constraints that restrain the production and perception of speech. Secondly, the phonetic realization of prosodic phrase boundaries is investigated in a study on articulation rate changes within the prosodic phrase. Evidence of phrase-final lengthening, a reduction of the articulation rate in the final part of the prosodic phrase, is found. The tonal means used to signal coherence within the prosodic phrase is subsequently investigated. An attempt is made to test the two Lund intonation modelsโ€™ capacities for describing spontaneous speech. The two approaches have different implications for the amount of preplanning needed, which makes them particularly interesting to compare by testing spontaneous data. The results indicate that no or little preplanning is needed to produce tonally coherent phrases. No evidence is found to suggest e.g. that speakers accommodate for the length of the upcoming phrase by starting longer phrases with a higher F0 than short phrases. An explanation is sought for variation in F0 starting points found in the data despite F0โ€™s insensitivity to phrase length. It is concluded that F0 is used to signal coherence even across prosodic phrase boundaries. It is furthermore found that tonal coherence signals are used to override strong boundary signals in spontaneous speech, thereby making initially unplanned additions possible. Finally, the perception of boundary strength is examined in two perception experiments. Listeners are found to agree well in their perceptual judgments of boundary strength, and it is shown that the main correlate to perceived boundary strength in spontaneous speech is pause length. The useful distinction between weak, prosodic phrase boundaries and strong, prosodic utterance boundaries in descriptions of read speech is found to be inappropriate for descriptions of spontaneous speech. It fails to capture the conflicting local and global signals of boundary strength and coherence that arise when strong boundary signals are overriden by coherence signals. The possibility to use conflicting signals in this way is seen as an important asset to the speaker as it makes changes in the speech plan possible, and it is regarded to be a characteristic of prosodic phrasing in spontaneous speech

    CAPT๋ฅผ ์œ„ํ•œ ๋ฐœ์Œ ๋ณ€์ด ๋ถ„์„ ๋ฐ CycleGAN ๊ธฐ๋ฐ˜ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต,2020. 2. ์ •๋ฏผํ™”.Despite the growing popularity in learning Korean as a foreign language and the rapid development in language learning applications, the existing computer-assisted pronunciation training (CAPT) systems in Korean do not utilize linguistic characteristics of non-native Korean speech. Pronunciation variations in non-native speech are far more diverse than those observed in native speech, which may pose a difficulty in combining such knowledge in an automatic system. Moreover, most of the existing methods rely on feature extraction results from signal processing, prosodic analysis, and natural language processing techniques. Such methods entail limitations since they necessarily depend on finding the right features for the task and the extraction accuracies. This thesis presents a new approach for corrective feedback generation in a CAPT system, in which pronunciation variation patterns and linguistic correlates with accentedness are analyzed and combined with a deep neural network approach, so that feature engineering efforts are minimized while maintaining the linguistically important factors for the corrective feedback generation task. Investigations on non-native Korean speech characteristics in contrast with those of native speakers, and their correlation with accentedness judgement show that both segmental and prosodic variations are important factors in a Korean CAPT system. The present thesis argues that the feedback generation task can be interpreted as a style transfer problem, and proposes to evaluate the idea using generative adversarial network. A corrective feedback generation model is trained on 65,100 read utterances by 217 non-native speakers of 27 mother tongue backgrounds. The features are automatically learnt in an unsupervised way in an auxiliary classifier CycleGAN setting, in which the generator learns to map a foreign accented speech to native speech distributions. In order to inject linguistic knowledge into the network, an auxiliary classifier is trained so that the feedback also identifies the linguistic error types that were defined in the first half of the thesis. The proposed approach generates a corrected version the speech using the learners own voice, outperforming the conventional Pitch-Synchronous Overlap-and-Add method.์™ธ๊ตญ์–ด๋กœ์„œ์˜ ํ•œ๊ตญ์–ด ๊ต์œก์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๊ณ ์กฐ๋˜์–ด ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์ˆ˜๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์Œ์„ฑ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ์ ์šฉํ•œ ์ปดํ“จํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐœ์Œ ๊ต์œก(Computer-Assisted Pronunciation Training; CAPT) ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๋˜ํ•œ ์ ๊ทน์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ˜„์กดํ•˜๋Š” ํ•œ๊ตญ์–ด ๋งํ•˜๊ธฐ ๊ต์œก ์‹œ์Šคํ…œ์€ ์™ธ๊ตญ์ธ์˜ ํ•œ๊ตญ์–ด์— ๋Œ€ํ•œ ์–ธ์–ดํ•™์  ํŠน์ง•์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์ง€ ์•Š๊ณ  ์žˆ์œผ๋ฉฐ, ์ตœ์‹  ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ  ๋˜ํ•œ ์ ์šฉ๋˜์ง€ ์•Š๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๊ฐ€๋Šฅํ•œ ์›์ธ์œผ๋กœ์จ๋Š” ์™ธ๊ตญ์ธ ๋ฐœํ™” ํ•œ๊ตญ์–ด ํ˜„์ƒ์— ๋Œ€ํ•œ ๋ถ„์„์ด ์ถฉ๋ถ„ํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ์žˆ์–ด๋„ ์ด๋ฅผ ์ž๋™ํ™”๋œ ์‹œ์Šคํ…œ์— ๋ฐ˜์˜ํ•˜๊ธฐ์—๋Š” ๊ณ ๋„ํ™”๋œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด ์žˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ CAPT ๊ธฐ์ˆ  ์ „๋ฐ˜์ ์œผ๋กœ๋Š” ์‹ ํ˜ธ์ฒ˜๋ฆฌ, ์šด์œจ ๋ถ„์„, ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•๊ณผ ๊ฐ™์€ ํŠน์ง• ์ถ”์ถœ์— ์˜์กดํ•˜๊ณ  ์žˆ์–ด์„œ ์ ํ•ฉํ•œ ํŠน์ง•์„ ์ฐพ๊ณ  ์ด๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ถœํ•˜๋Š” ๋ฐ์— ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•œ ์‹ค์ •์ด๋‹ค. ์ด๋Š” ์ตœ์‹  ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ์ด ๊ณผ์ • ๋˜ํ•œ ๋ฐœ์ „์˜ ์—ฌ์ง€๊ฐ€ ๋งŽ๋‹ค๋Š” ๋ฐ”๋ฅผ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋จผ์ € CAPT ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์— ์žˆ์–ด ๋ฐœ์Œ ๋ณ€์ด ์–‘์ƒ๊ณผ ์–ธ์–ดํ•™์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์™ธ๊ตญ์ธ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ๊ณผ ํ•œ๊ตญ์–ด ์›์–ด๋ฏผ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ์„ ๋Œ€์กฐํ•˜๊ณ  ์ฃผ์š”ํ•œ ๋ณ€์ด๋ฅผ ํ™•์ธํ•œ ํ›„, ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์˜์‚ฌ์†Œํ†ต์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”๋„๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ข…์„ฑ ์‚ญ์ œ์™€ 3์ค‘ ๋Œ€๋ฆฝ์˜ ํ˜ผ๋™, ์ดˆ๋ถ„์ ˆ ๊ด€๋ จ ์˜ค๋ฅ˜๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ์— ์šฐ์„ ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ต์ •๋œ ํ”ผ๋“œ๋ฐฑ์„ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์€ CAPT ์‹œ์Šคํ…œ์˜ ์ค‘์š”ํ•œ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด ๊ณผ์ œ๊ฐ€ ๋ฐœํ™”์˜ ์Šคํƒ€์ผ ๋ณ€ํ™”์˜ ๋ฌธ์ œ๋กœ ํ•ด์„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณด์•˜์œผ๋ฉฐ, ์ƒ์„ฑ์  ์ ๋Œ€ ์‹ ๊ฒฝ๋ง (Cycle-consistent Generative Adversarial Network; CycleGAN) ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. GAN ๋„คํŠธ์›Œํฌ์˜ ์ƒ์„ฑ๋ชจ๋ธ์€ ๋น„์›์–ด๋ฏผ ๋ฐœํ™”์˜ ๋ถ„ํฌ์™€ ์›์–ด๋ฏผ ๋ฐœํ™” ๋ถ„ํฌ์˜ ๋งคํ•‘์„ ํ•™์Šตํ•˜๋ฉฐ, Cycle consistency ์†์‹คํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋ฐœํ™”๊ฐ„ ์ „๋ฐ˜์ ์ธ ๊ตฌ์กฐ๋ฅผ ์œ ์ง€ํ•จ๊ณผ ๋™์‹œ์— ๊ณผ๋„ํ•œ ๊ต์ •์„ ๋ฐฉ์ง€ํ•˜์˜€๋‹ค. ๋ณ„๋„์˜ ํŠน์ง• ์ถ”์ถœ ๊ณผ์ •์ด ์—†์ด ํ•„์š”ํ•œ ํŠน์ง•๋“ค์ด CycleGAN ํ”„๋ ˆ์ž„์›Œํฌ์—์„œ ๋ฌด๊ฐ๋… ๋ฐฉ๋ฒ•์œผ๋กœ ์Šค์Šค๋กœ ํ•™์Šต๋˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ์–ธ์–ด ํ™•์žฅ์ด ์šฉ์ดํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ์–ธ์–ดํ•™์  ๋ถ„์„์—์„œ ๋“œ๋Ÿฌ๋‚œ ์ฃผ์š”ํ•œ ๋ณ€์ด๋“ค ๊ฐ„์˜ ์šฐ์„ ์ˆœ์œ„๋Š” Auxiliary Classifier CycleGAN ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ CycleGAN์— ์ง€์‹์„ ์ ‘๋ชฉ์‹œ์ผœ ํ”ผ๋“œ๋ฐฑ ์Œ์„ฑ์„ ์ƒ์„ฑํ•จ๊ณผ ๋™์‹œ์— ํ•ด๋‹น ํ”ผ๋“œ๋ฐฑ์ด ์–ด๋–ค ์œ ํ˜•์˜ ์˜ค๋ฅ˜์ธ์ง€ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Š” ๋„๋ฉ”์ธ ์ง€์‹์ด ๊ต์ • ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ ๋‹จ๊ณ„๊นŒ์ง€ ์œ ์ง€๋˜๊ณ  ํ†ต์ œ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค๋Š” ๋ฐ์— ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ 27๊ฐœ์˜ ๋ชจ๊ตญ์–ด๋ฅผ ๊ฐ–๋Š” 217๋ช…์˜ ์œ ์˜๋ฏธ ์–ดํœ˜ ๋ฐœํ™” 65,100๊ฐœ๋กœ ํ”ผ๋“œ๋ฐฑ ์ž๋™ ์ƒ์„ฑ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๊ณ , ๊ฐœ์„  ์—ฌ๋ถ€ ๋ฐ ์ •๋„์— ๋Œ€ํ•œ ์ง€๊ฐ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€์„ ๋•Œ ํ•™์Šต์ž ๋ณธ์ธ์˜ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ์œ ์ง€ํ•œ ์ฑ„ ๊ต์ •๋œ ๋ฐœ์Œ์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ•์ธ ์Œ๋†’์ด ๋™๊ธฐ์‹ ์ค‘์ฒฉ๊ฐ€์‚ฐ (Pitch-Synchronous Overlap-and-Add) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ์ƒ๋Œ€ ๊ฐœ์„ ๋ฅ  16.67%์ด ํ™•์ธ๋˜์—ˆ๋‹ค.Chapter 1. Introduction 1 1.1. Motivation 1 1.1.1. An Overview of CAPT Systems 3 1.1.2. Survey of existing Korean CAPT Systems 5 1.2. Problem Statement 7 1.3. Thesis Structure 7 Chapter 2. Pronunciation Analysis of Korean Produced by Chinese 9 2.1. Comparison between Korean and Chinese 11 2.1.1. Phonetic and Syllable Structure Comparisons 11 2.1.2. Phonological Comparisons 14 2.2. Related Works 16 2.3. Proposed Analysis Method 19 2.3.1. Corpus 19 2.3.2. Transcribers and Agreement Rates 22 2.4. Salient Pronunciation Variations 22 2.4.1. Segmental Variation Patterns 22 2.4.1.1. Discussions 25 2.4.2. Phonological Variation Patterns 26 2.4.1.2. Discussions 27 2.5. Summary 29 Chapter 3. Correlation Analysis of Pronunciation Variations and Human Evaluation 30 3.1. Related Works 31 3.1.1. Criteria used in L2 Speech 31 3.1.2. Criteria used in L2 Korean Speech 32 3.2. Proposed Human Evaluation Method 36 3.2.1. Reading Prompt Design 36 3.2.2. Evaluation Criteria Design 37 3.2.3. Raters and Agreement Rates 40 3.3. Linguistic Factors Affecting L2 Korean Accentedness 41 3.3.1. Pearsons Correlation Analysis 41 3.3.2. Discussions 42 3.3.3. Implications for Automatic Feedback Generation 44 3.4. Summary 45 Chapter 4. Corrective Feedback Generation for CAPT 46 4.1. Related Works 46 4.1.1. Prosody Transplantation 47 4.1.2. Recent Speech Conversion Methods 49 4.1.3. Evaluation of Corrective Feedback 50 4.2. Proposed Method: Corrective Feedback as a Style Transfer 51 4.2.1. Speech Analysis at Spectral Domain 53 4.2.2. Self-imitative Learning 55 4.2.3. An Analogy: CAPT System and GAN Architecture 57 4.3. Generative Adversarial Networks 59 4.3.1. Conditional GAN 61 4.3.2. CycleGAN 62 4.4. Experiment 63 4.4.1. Corpus 64 4.4.2. Baseline Implementation 65 4.4.3. Adversarial Training Implementation 65 4.4.4. Spectrogram-to-Spectrogram Training 66 4.5. Results and Evaluation 69 4.5.1. Spectrogram Generation Results 69 4.5.2. Perceptual Evaluation 70 4.5.3. Discussions 72 4.6. Summary 74 Chapter 5. Integration of Linguistic Knowledge in an Auxiliary Classifier CycleGAN for Feedback Generation 75 5.1. Linguistic Class Selection 75 5.2. Auxiliary Classifier CycleGAN Design 77 5.3. Experiment and Results 80 5.3.1. Corpus 80 5.3.2. Feature Annotations 81 5.3.3. Experiment Setup 81 5.3.4. Results 82 5.4. Summary 84 Chapter 6. Conclusion 86 6.1. Thesis Results 86 6.2. Thesis Contributions 88 6.3. Recommendations for Future Work 89 Bibliography 91 Appendix 107 Abstract in Korean 117 Acknowledgments 120Docto

    Spartan Daily, March 2, 1993

    Get PDF
    Volume 100, Issue 22https://scholarworks.sjsu.edu/spartandaily/8380/thumbnail.jp

    Speech Recognition

    Get PDF
    Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
    • โ€ฆ
    corecore