4 research outputs found
Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization
Techniques for unsupervised discovery of acoustic patterns are getting
increasingly attractive, because huge quantities of speech data are becoming
available but manual annotations remain hard to acquire. In this paper, we
propose an approach for unsupervised discovery of linguistic structure for the
target spoken language given raw speech data. This linguistic structure
includes two-level (subword-like and word-like) acoustic patterns, the lexicon
of word-like patterns in terms of subword-like patterns and the N-gram language
model based on word-like patterns. All patterns, models, and parameters can be
automatically learned from the unlabelled speech corpus. This is achieved by an
initialization step followed by three cascaded stages for acoustic, linguistic,
and lexical iterative optimization. The lexicon of word-like patterns defines
allowed consecutive sequence of HMMs for subword-like patterns. In each
iteration, model training and decoding produces updated labels from which the
lexicon and HMMs can be further updated. In this way, model parameters and
decoded labels are respectively optimized in each iteration, and the knowledge
about the linguistic structure is learned gradually layer after layer. The
proposed approach was tested in preliminary experiments on a corpus of Mandarin
broadcast news, including a task of spoken term detection with performance
compared to a parallel test using models trained in a supervised way. Results
show that the proposed system not only yields reasonable performance on its
own, but is also complimentary to existing large vocabulary ASR systems.Comment: Accepted by ICASSP 201
Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection
In this paper, we compare two paradigms for unsupervised discovery of
structured acoustic tokens directly from speech corpora without any human
annotation. The Multigranular Paradigm seeks to capture all available
information in the corpora with multiple sets of tokens for different model
granularities. The Hierarchical Paradigm attempts to jointly learn several
levels of signal representations in a hierarchical structure. The two paradigms
are unified within a theoretical framework in this paper. Query-by-Example
Spoken Term Detection (QbE-STD) experiments on the QUESST dataset of MediaEval
2015 verifies the competitiveness of the acoustic tokens. The Enhanced
Relevance Score (ERS) proposed in this work improves both paradigms for the
task of QbE-STD. We also list results on the ABX evaluation task of the Zero
Resource Challenge 2015 for comparison of the Paradigms
Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection
In this paper we aim to automatically discover high quality frame-level
speech features and acoustic tokens directly from unlabeled speech data. A
Multi-granular Acoustic Tokenizer (MAT) was proposed for automatic discovery of
multiple sets of acoustic tokens from the given corpus. Each acoustic token set
is specified by a set of hyperparameters describing the model configuration.
These different sets of acoustic tokens carry different characteristics for the
given corpus and the language behind, thus can be mutually reinforced. The
multiple sets of token labels are then used as the targets of a Multi-target
Deep Neural Network (MDNN) trained on frame-level acoustic features. Bottleneck
features extracted from the MDNN are then used as the feedback input to the MAT
and the MDNN itself in the next iteration. The multi-granular acoustic token
sets and the frame-level speech features can be iteratively optimized in the
iterative deep learning framework. We call this framework the Multi-granular
Acoustic Tokenizing Deep Neural Network (MATDNN). The results were evaluated
using the metrics and corpora defined in the Zero Resource Speech Challenge
organized at Interspeech 2015, and improved performance was obtained with a set
of experiments of query-by-example spoken term detection on the same corpora.
Visualization for the discovered tokens against the English phonemes was also
shown.Comment: Accepted by IEEE/ACM Transactions on Audio Speech and Language
Processing. arXiv admin note: text overlap with arXiv:1602.00426,
arXiv:1506.0232
Exploiting Cross-Lingual Knowledge in Unsupervised Acoustic Modeling for Low-Resource Languages
(Short version of Abstract) This thesis describes an investigation on
unsupervised acoustic modeling (UAM) for automatic speech recognition (ASR) in
the zero-resource scenario, where only untranscribed speech data is assumed to
be available. UAM is not only important in addressing the general problem of
data scarcity in ASR technology development but also essential to many
non-mainstream applications, for examples, language protection, language
acquisition and pathological speech assessment. The present study is focused on
two research problems. The first problem concerns unsupervised discovery of
basic (subword level) speech units in a given language. Under the zero-resource
condition, the speech units could be inferred only from the acoustic signals,
without requiring or involving any linguistic direction and/or constraints. The
second problem is referred to as unsupervised subword modeling. In its essence
a frame-level feature representation needs to be learned from untranscribed
speech. The learned feature representation is the basis of subword unit
discovery. It is desired to be linguistically discriminative and robust to
non-linguistic factors. Particularly extensive use of cross-lingual knowledge
in subword unit discovery and modeling is a focus of this research.Comment: Ph.D. Thesis Submitted in May 2020 in partial fulfilment of the
requirements for the Degree of Doctor of Philosophy in Electronic
Engineering, The Chinese University of Hong Kong (CUHK) 134 page