346 research outputs found
Attention-Based End-to-End Speech Recognition on Voice Search
Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that applying attention-based encoder-decoder to Mandarin speech
recognition was quite difficult due to the logographic orthography of Mandarin,
the large vocabulary and the conditional dependency of the attention model. In
this paper, we use character embedding to deal with the large vocabulary.
Several tricks are used for effective model training, including L2
regularization, Gaussian weight noise and frame skipping. We compare two
attention mechanisms and use attention smoothing to cover long context in the
attention model. Taken together, these tricks allow us to finally achieve a
character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on
the MiTV voice search dataset. While together with a trigram language model,
CER and SER reach 2.81% and 5.77%, respectively
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
The lack of code-switch training data is one of the major concerns in the
development of end-to-end code-switching automatic speech recognition (ASR)
models. In this work, we propose a method to train an improved end-to-end
code-switching ASR using only monolingual data. Our method encourages the
distributions of output token embeddings of monolingual languages to be
similar, and hence, promotes the ASR model to easily code-switch between
languages. Specifically, we propose to use Jensen-Shannon divergence and cosine
distance based constraints. The former will enforce output embeddings of
monolingual languages to possess similar distributions, while the later simply
brings the centroids of two distributions to be close to each other.
Experimental results demonstrate high effectiveness of the proposed method,
yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English
code-switching ASR task.Comment: 5 pages, 3 figures, accepted to INTERSPEECH 201
Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method
The automatic identification and analysis of pronunciation errors, known as
Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer
Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning
or speech therapy applications. Existing MDD methods relying on analysing
phonemes can only detect categorical errors of phonemes that have an adequate
amount of training data to be modelled. With the unpredictable nature of the
pronunciation errors of non-native or disordered speakers and the scarcity of
training datasets, it is unfeasible to model all types of mispronunciations.
Moreover, phoneme-level MDD approaches have a limited ability to provide
detailed diagnostic information about the error made. In this paper, we propose
a low-level MDD approach based on the detection of speech attribute features.
Speech attribute features break down phoneme production into elementary
components that are directly related to the articulatory system leading to more
formative feedback to the learner. We further propose a multi-label variant of
the Connectionist Temporal Classification (CTC) approach to jointly model the
non-mutually exclusive speech attributes using a single model. The pre-trained
wav2vec2 model was employed as a core model for the speech attribute detector.
The proposed method was applied to L2 speech corpora collected from English
learners from different native languages. The proposed speech attribute MDD
method was further compared to the traditional phoneme-level MDD and achieved a
significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR),
and Diagnostic Error Rate (DER) over all speech attributes compared to the
phoneme-level equivalent
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
When labeled data is insufficient, semi-supervised learning with the
pseudo-labeling technique can significantly improve the performance of
automatic speech recognition. However, pseudo-labels are often noisy,
containing numerous incorrect tokens. Taking noisy labels as ground-truth in
the loss function results in suboptimal performance. Previous works attempted
to mitigate this issue by either filtering out the nosiest pseudo-labels or
improving the overall quality of pseudo-labels. While these methods are
effective to some extent, it is unrealistic to entirely eliminate incorrect
tokens in pseudo-labels. In this work, we propose a novel framework named
alternative pseudo-labeling to tackle the issue of noisy pseudo-labels from the
perspective of the training objective. The framework comprises several
components. Firstly, a generalized CTC loss function is introduced to handle
noisy pseudo-labels by accepting alternative tokens in the positions of
incorrect tokens. Applying this loss function in pseudo-labeling requires
detecting incorrect tokens in the predicted pseudo-labels. In this work, we
adopt a confidence-based error detection method that identifies the incorrect
tokens by comparing their confidence scores with a given threshold, thus
necessitating the confidence score to be discriminative. Hence, the second
proposed technique is the contrastive CTC loss function that widens the
confidence gap between the correctly and incorrectly predicted tokens, thereby
improving the error detection ability. Additionally, obtaining satisfactory
performance with confidence-based error detection typically requires extensive
threshold tuning. Instead, we propose an automatic thresholding method that
uses labeled data as a proxy for determining the threshold, thus saving the
pain of manual tuning.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition
Long short-term memory (LSTM) based acoustic modeling methods have recently
been shown to give state-of-the-art performance on some speech recognition
tasks. To achieve a further performance improvement, in this research, deep
extensions on LSTM are investigated considering that deep hierarchical model
has turned out to be more efficient than a shallow one. Motivated by previous
research on constructing deep recurrent neural networks (RNNs), alternative
deep LSTM architectures are proposed and empirically evaluated on a large
vocabulary conversational telephone speech recognition task. Meanwhile,
regarding to multi-GPU devices, the training process for LSTM networks is
introduced and discussed. Experimental results demonstrate that the deep LSTM
networks benefit from the depth and yield the state-of-the-art performance on
this task.Comment: submitted to ICASSP 2015 which does not perform blind review
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