Speech signals are inherently complex as they encompass both global acoustic characteristics and local semantic information. However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction network. To overcome this challenge, we propose a self-supervised disentangled representation learning method. Our approach tackles this issue through a two-phase process, utilizing a reference speech encoding network and a global information disentanglement network to gradually disentangle the speaker identity information from other irrelevant factors. We exclusively employ the disentangled speaker identity information to guide the speech extraction network. Moreover, we introduce the adaptive modulation Transformer to ensure that the acoustic representation of the mixed signal remains undisturbed by the speaker embeddings. This component incorporates speaker embeddings as conditional information, facilitating natural and efficient guidance for the speech extraction network. Experimental results substantiate the effectiveness of our meticulously crafted approach, showcasing a substantial reduction in the likelihood of speaker confusion.

VConformer-based attention models have become the de facto backbone model for Automatic Speech Recognition tasks. A blank symbol is usually introduced to align the input and output sequences for CTC or RNN-T models. Unfortunately, the long input length overloads computational budget and memory consumption quadratically by attention mechanism. In this work, we propose a "Skip-and-Recover" Conformer architecture, named Skipformer, to squeeze sequence input length dynamically and inhomogeneously. Skipformer uses an intermediate CTC output as criteria to split frames into three groups: crucial, skipping and ignoring. The crucial group feeds into next conformer blocks and its output joint with skipping group by original temporal order as the final encoder output. Experiments show that our model reduces the input sequence length by 31 times on Aishell-1 and 22 times on Librispeech corpus. Meanwhile, the model can achieve better recognition accuracy and faster inference speed than recent baseline models. Our code is available at https://github.com/Duxiaoman-DI/public-achievements-on-Speech/tree/skipformer.

Recently, Conformer as a backbone network for end-to-end automatic speech recognition achieved state-of-the-art performance. The Conformer block leverages a self-attention mechanism to capture global information, along with a convolutional neural network to capture local information, resulting in improved performance. However, the Conformer-based model encounters an issue with the self-attention mechanism, as computational complexity grows quadratically with the length of the input sequence. Inspired by previous Connectionist Temporal Classification (CTC) guided blank skipping during decoding, we introduce intermediate CTC outputs as guidance into the downsampling procedure of the Conformer encoder. We define the frame with non-blank output as key frame. Specifically, we introduce the key frame-based self-attention (KFSA) mechanism, a novel method to reduce the computation of the self-attention mechanism using key frames. The structure of our proposed approach comprises two encoders. Following the initial encoder, we introduce an intermediate CTC loss function to compute the label frame, enabling us to extract the key frames and blank frames for KFSA. Furthermore, we introduce the key frame-based downsampling (KFDS) mechanism to operate on high-dimensional acoustic features directly and drop the frames corresponding to blank labels, which results in new acoustic feature sequences as input to the second encoder. By using the proposed method, which achieves comparable or higher performance than vanilla Conformer and other similar work such as Efficient Conformer. Meantime, our proposed method can discard more than 60% useless frames during model training and inference, which will accelerate the inference speed significantly.

Conversational automatic speech recognition (ASR) is a task to recognize conversational speech including multiple speakers. Unlike sentence-level ASR, conversational ASR can naturally take advantages from specific characteristics of conversation, such as role preference and topical coherence. This paper proposes a conversational ASR model which explicitly learns conversation-level characteristics under the prevalent end-to-end neural framework. The highlights of the proposed model are twofold. First, a latent variational module (LVM) is attached to a conformer-based encoder-decoder ASR backbone to learn role preference and topical coherence. Second, a topic model is specifically adopted to bias the outputs of the decoder to words in the predicted topics. Experiments on two Mandarin conversational ASR tasks show that the proposed model achieves a maximum 12% relative character error rate (CER) reduction.

Leveraging context information is an intuitive idea to improve performance on conversational automatic speech recognition(ASR). Previous works usually adopt recognized hypotheses of historical utterances as preceding context, which may bias the current recognized hypothesis due to the inevitable historicalrecognition errors. To avoid this problem, we propose an audio-textual cross-modal representation extractor to learn contextual representations directly from preceding speech. Specifically, it consists of two modal-related encoders, extracting high-level latent features from speech and the corresponding text, and a cross-modal encoder, which aims to learn the correlation between speech and text. We randomly mask some input tokens and input sequences of each modality. Then a token-missing or modal-missing prediction with a modal-level CTC loss on the cross-modal encoder is performed. Thus, the model captures not only the bi-directional context dependencies in a specific modality but also relationships between different modalities. Then, during the training of the conversational ASR system, the extractor will be frozen to extract the textual representation of preceding speech, while such representation is used as context fed to the ASR decoder through attention mechanism. The effectiveness of the proposed approach is validated on several Mandarin conversation corpora and the highest character error rate (CER) reduction up to 16% is achieved on the MagicData dataset.

Customized keyword spotting (KWS) has great potential to be deployed on edge devices to achieve hands-free user experience. However, in real applications, false alarm (FA) would be a serious problem for spotting dozens or even hundreds of keywords, which drastically affects user experience. To solve this problem, in this paper, we leverage the recent advances in transducer and transformer based acoustic models and propose a new multi-stage customized KWS framework named Cascaded Transducer-Transformer KWS (CaTT-KWS), which includes a transducer based keyword detector, a frame-level phone predictor based force alignment module and a transformer based decoder. Specifically, the streaming transducer module is used to spot keyword candidates in audio stream. Then force alignment is implemented using the phone posteriors predicted by the phone predictor to finish the first stage keyword verification and refine the time boundaries of keyword. Finally, the transformer decoder further verifies the triggered keyword. Our proposed CaTT-KWS framework reduces FA rate effectively without obviously hurting keyword recognition accuracy. Specifically, we can get impressively 0.13 FA per hour on a challenging dataset, with over 90% relative reduction on FA comparing to the transducer based detection model, while keyword recognition accuracy only drops less than 2%.

This paper proposes an extremely lightweight phone-based transducer model with a tiny decoding graph on edge devices. First, a phone synchronous decoding (PSD) algorithm based on blank label skipping is first used to speed up the transducer decoding process. Then, to decrease the deletion errors introduced by the high blank score, a blank label deweighting approach is proposed. To reduce parameters and computation, deep feedforward sequential memory network (DFSMN) layers are used in the transducer encoder, and a CNN-based stateless predictor is adopted. SVD technology compresses the model further. WFST-based decoding graph takes the context-independent (CI) phone posteriors as input and allows us to flexibly bias user-specific information. Finally, with only 0.9M parameters after SVD, our system could give a relative 9.1% - 20.5% improvement compared with a bigger conventional hybrid system on edge devices.

Recently self-supervised learning has emerged as an effective approach to improve the performance of automatic speech recognition (ASR).Under such a framework, the neural network is usually pre-trained with massive unlabeled data and then fine-tuned with limited labeled data.However, the nonstreaming architecture like bidirectional transformer is usually adopted by the neural network to achieve competitive results, which can not be used in streaming scenarios.In this paper, we mainly focus on improving the performance of streaming transformer under the self-supervised learning framework.Specifically, we propose a novel two-stage training method during finetuning, which combines knowledge distilling and self-training.The proposed training method achieves 16.3% relative word error rate (WER) reduction on Librispeech noisy test set.Finally, by only using the 100h clean subset of Librispeech as the labeled data and the rest (860h) as the unlabeled data, our streaming transformer based model obtains competitive WERs 3.5/8.7 on Librispeech clean/noisy test sets.

End-to-end models are favored in automatic speech recognition (ASR) because of their simplified system structure and superior performance.Among these models, Transformer and Conformer have achieved state-of-the-art recognition accuracy in which self-attention plays a vital role in capturing important global information.However, the time and memory complexity of self-attention increases squarely with the length of the sentence.In this paper, a prob-sparse self-attention mechanism is introduced into Conformer to sparse the computing process of self-attention in order to accelerate inference speed and reduce space consumption.Specifically, we adopt a Kullback-Leibler divergence based sparsity measurement for each query to decide whether we compute the attention function on this query.By using the prob-sparse attention mechanism, we achieve impressively 8% to 45% inference speed-up and 15% to 45% memory usage reduction of the self-attention module of Conformer Transducer while maintaining the same level of error rate.

Serving as the tigger of a voice-enabled user interface, on-device keyword spotting model has to be extremely compact, efficient and accurate. In this paper, we adopt a depth-wise separable convolutional neural network (DS-CNN) as our small-footprint KWS model, which is highly competitive to these ends. However, recent study has shown that a compact KWS system is very vulnerable to small adversarial perturbations while augmenting the training data with specifically-generated adversarial examples can improve performance. In this paper, we further improve KWS performance through a virtual adversarial training (VAT) solution. Instead of using adversarial examples for data augmentation, we propose to train a DS-CNN KWS model using adversarial regularization, which aims to smooth model's distribution and thus to improve robustness, by explicitly introducing a distribution smoothness measure into the loss function. Experiments on a collected KWS corpus using a circular microphone array in far-field scenario show that the VAT approach brings 31.9% relative false rejection rate (FRR) reduction compared to the normal training approach with cross entropy loss, and it also surpasses the adversarial example based data augmentation approach with 10.3% relative FRR reduction.

This paper proposes an attention-based neural network approach for single channel speech enhancement. Our work is inspired by the recent success of attention models in sequence-to-sequence learning. It is intuitive to use attention mechanism in speech enhancement as humans are able to focus on the important speech components in an audio stream with "high attention" while perceiving the unimportant region (e.g., noise or interference) in "low attention", and thus adjust the focal point over time. Specifically, taking noisy spectrum as input, our model is composed of an LSTM based encoder, an attention mechanism and a speech generator, resulting in enhanced spectrum. Experiments show that, as compared with OM-LSA and the LSTM baseline, the proposed attention approach can consistently achieve better performance in terms of speech quality (PESQ) and intelligibility (STOI). More promisingly, the attention-based approach has better generalization ability to unseen noise conditions.

A second language (L2) learner usually cannot speak L2 well in both pronunciations and forming-of-words. Hence his/her L2 speech cannot be well recognized by a recognizer trained with native data. Domain adversarial training (DAT), capable of reducing the acoustic mismatch between training and testing, can be useful for improving speech recognition of L2 learners. To get around the ungrammatical L2 speech in scenario-based conversation training, keyword spotting (KWS) is an effective solution by relaxing the language model constraint in decoding. On the acoustic pronunciation side, DAT is investigated in this study for training a neural net-based acoustic model. DAT model is trained with both native and English as second language (ESL) learners' speech to extract more invariant features from native to ESL speech by equalizing their intrinsic difference. The model is jointly optimized for improved senone classification in training. Testing on ESL learners' speech and native English, the DAT model improves recognition performance which is comparable to jointly trained multi-condition model but significantly improves the performance of native speech recognition. In KWS, DAT shows a consistent better performance than the multi-condition training. The improved performance of proposed model is also obtained without increasing its computation complexity or the model size.

In this paper, we explore the use of adversarial examples for improving a neural network based keyword spotting (KWS) system. Specially, in our system, an effective and small-footprint attention-based neural network model is used. Adversarial example is defined as a misclassified example by a model, but it is only slightly skewed from the original correctly-classified one. In the KWS task, it is a natural idea to regard the false alarmed or false rejected queries as some kind of adversarial examples. In our work, given a well-trained attention-based KWS model, we first generate adversarial examples using the fast gradient sign method (FGSM) and find that these examples can dramatically degrade the KWS performance. Using these adversarial examples as augmented data to retrain the KWS model, we finally achieve 45.6% relative and false reject rate (FRR) reduction at 1.0 false alarm rate (FAR) per hour on a collected dataset from a smart speaker.

End-to-end speech recognition, such as attention based approaches, is an emerging and attractive topic in recent years. It has achieved comparable performance with the traditional speech recognition framework. Because end-to-end approaches integrate acoustic and linguistic information into one model, the perturbation in the acoustic level such as acoustic noise, could be easily propagated to the linguistic level. Thus improving model robustness in real application environments for these end-to-end systems is crucial. In this paper, in order to make the attention based end-to-end model more robust against noises, we formulate regulation of the objective function with adversarial training examples. Particularly two adversarial regularization techniques, the fast gradient-sign method and the local distributional smoothness method, are explored to improve noise robustness. Experiments on two publicly available Chinese Mandarin corpora, AISHELL-1 and AISHELL-2, show that adversarial regularization is an effective approach to improve robustness against noises for our attention-based models. Specifically, we obtained 18.4% relative character error rate (CER) reduction on the AISHELL-1 noisy test set. Even on the clean test set, we showed 16.7% relative improvement. As the training set increases and covers more environmental varieties, our proposed methods remain effective despite that the improvement shrinks. Training on the large AISHELL-2 training corpus and testing on the various AISHELL-2 test sets, we achieved 7.0%-12.2% relative error rate reduction. To our knowledge, this is the first successful application of adversarial regularization to sequence-to-sequence speech recognition systems.

In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data (“standard” accent) and unlabeled target domain data (with heavy accents), we augment the learning objective for a Kaldi TDNN network with a domain adversarial training (DAT) objective to encourage the model to learn accent-invariant features. In experiments with three Mandarin accents, we show that DAT yields up to 7.45% relative character error rate reduction when we do not have transcriptions of the accented speech, compared with the baseline trained on standard accent data only. We also find a benefit from DAT when used in combination with training from automatic transcriptions on the accented data. Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition.

The i-vector approach to speaker recognition has achieved good performance when the domain of the evaluation dataset is similar to that of the training dataset. However, in realworld applications, there is always a mismatch between the training and evaluation datasets, that leads to performance degradation. To address this problem, this paper proposes to learn the domain-invariant and speaker-discriminative speech representations via domain adversarial training. Specifically, with domain adversarial training method, we use a gradient reversal layer to remove the domain variation and project the different domain data into the same subspace. Moreover, we compare the proposed method with other state-of-the-art unsupervised domain adaptation techniques for i-vector approach to speaker recognition (e.g. autoencoder based domain adaptation, inter dataset variability compensation, dataset-invariant covariance normalization, and so on). Experiments on 2013 domain adaptation challenge (DAC) dataset demonstrate that the proposed method is not only effective in solving the dataset mismatch problem, but also outperforms the compared unsupervised domain adaptation methods.

This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data.Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters.We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against noise and channel variation.Further improvement is obtained when combining adversarial examples with teacher/student training, leading to a 23% relative word error rate reduction on Aurora-4.

We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%-19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model.