484 research outputs found
Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries
In Japanese text-to-speech (TTS), it is necessary to add accent information
to the input sentence. However, there are a limited number of publicly
available accent dictionaries, and those dictionaries e.g. UniDic, do not
contain many compound words, proper nouns, etc., which are required in a
practical TTS system. In order to build a large scale accent dictionary that
contains those words, the authors developed an accent estimation technique that
predicts the accent of a word from its limited information, namely the surface
(e.g. kanji) and the yomi (simplified phonetic information). It is
experimentally shown that the technique can estimate accents with high
accuracies, especially for some categories of words. The authors applied this
technique to an existing large vocabulary Japanese dictionary NEologd, and
obtained a large vocabulary Japanese accent dictionary. Many cases have been
observed in which the use of this dictionary yields more appropriate phonetic
information than UniDic.Comment: 7 pages, 2 figures. IEEE ICASSP 202
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
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Deep Learning for Automatic Assessment and Feedback of Spoken English
Growing global demand for learning a second language (L2), particularly English, has led to
considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications.
This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One
of the challenges in automatic spoken language assessment is giving candidates feedback on
particular aspects, or views, of their spoken language proficiency, in addition to the overall
holistic score normally provided. Another is detecting pronunciation and other types of errors
at the word or utterance level and feeding them back to the learner in a useful way.
It is usually difficult to obtain accurate training data with separate scores for different
views and, as examiners are often trained to give holistic grades, single-view scores can
suffer issues of consistency. Conversely, holistic scores are available for various standard
assessment tasks such as Linguaskill. An investigation is thus conducted into whether
assessment scores linked to particular views of the speaker’s ability can be obtained from
systems trained using only holistic scores.
End-to-end neural systems are designed with structures and forms of input tuned to single
views, specifically each of pronunciation, rhythm, intonation and text. By training each
system on large quantities of candidate data, individual-view information should be possible
to extract. The relationships between the predictions of each system are evaluated to examine
whether they are, in fact, extracting different information about the speaker. Three methods
of combining the systems to predict holistic score are investigated, namely averaging their
predictions and concatenating and attending over their intermediate representations. The
combined graders are compared to each other and to baseline approaches.
The tasks of error detection and error tendency diagnosis become particularly challenging
when the speech in question is spontaneous and particularly given the challenges posed by
the inconsistency of human annotation of pronunciation errors. An approach to these tasks is
presented by distinguishing between lexical errors, wherein the speaker does not know how a
particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits
consistent patterns of phone substitution, deletion and insertion. Three annotated corpora
x
of non-native English speech by speakers of multiple L1s are analysed, the consistency of
human annotation investigated and a method presented for detecting individual accent and
lexical errors and diagnosing accent error tendencies at the speaker level
Statistical Parametric Methods for Articulatory-Based Foreign Accent Conversion
Foreign accent conversion seeks to transform utterances from a non-native speaker (L2) to appear as if they had been produced by the same speaker but with a native (L1) accent. Such accent-modified utterances have been suggested to be effective in pronunciation training for adult second language learners. Accent modification involves separating the linguistic gestures and voice-quality cues from the L1 and L2 utterances, then transposing them across the two speakers. However, because of the complex interaction between these two sources of information, their separation in the acoustic domain is not straightforward. As a result, vocoding approaches to accent conversion results in a voice that is different from both the L1 and L2 speakers. In contrast, separation in the articulatory domain is straightforward since linguistic gestures are readily available via articulatory data. However, because of the difficulty in collecting articulatory data, conventional synthesis techniques based on unit selection are ill-suited for accent conversion given the small size of articulatory corpora and the inability to interpolate missing native sounds in L2 corpus.
To address these issues, this dissertation presents two statistical parametric methods to accent conversion that operate in the acoustic and articulatory domains, respectively. The acoustic method uses a cross-speaker statistical mapping to generate L2 acoustic features from the trajectories of L1 acoustic features in a reference utterance. Our results show significant reductions in the perceived non-native accents compared to the corresponding L2 utterance. The results also show a strong voice-similarity between accent conversions and the original L2 utterance. Our second (articulatory-based) approach consists of building a statistical parametric articulatory synthesizer for a non-native speaker, then driving the synthesizer with the articulators from the reference L1 speaker. This statistical approach not only has low data requirements but also has the flexibility to interpolate missing sounds in the L2 corpus. In a series of listening tests, articulatory accent conversions were rated more intelligible and less accented than their L2 counterparts. In the final study, we compare the two approaches: acoustic and articulatory. Our results show that the articulatory approach, despite the direct access to the native linguistic gestures, is less effective in reducing perceived non-native accents than the acoustic approach
Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information
This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech
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