TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.
Recently, LSTMs have been particularly successful for automatic speech recognition @cite_1 . They have also been applied recently to speaker adaptation for acoustic modelling @cite_6 @cite_11 . However, to the best of our knowledge, it is the first time they are used for an actual speaker comparison task, and for speaker turn embedding.
@cite_1 @cite_6 @cite_11
2245630067 2400607496 1929897159 2428702538
Long Short-Term Memory (LSTM) recurrent neural network has proven effective in modeling speech and has achieved outstanding performance in both speech enhancement (SE) and automatic speech recognition (ASR). To further improve the performance of noise-robust speech recognition, a combination of speech enhancement and recognition was shown to be promising in earlier work. This paper aims to explore options for consistent integration of SE and ASR using LSTM networks. Since SE and ASR have different objective criteria, it is not clear what kind of integration would finally lead to the best word error rate for noise-robust ASR tasks. In this work, several integration architectures are proposed and tested, including: (1) a pipeline architecture of LSTM-based SE and ASR with sequence training, (2) an alternating estimation architecture, and (3) a multi-task hybrid LSTM network architecture. The proposed models were evaluated on the 2nd CHiME speech separation and recognition challenge task, and show significant improvements relative to prior results. Recently Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over deep neural networks (DNN) models in speech recognition and many other tasks. Although a lot of work have been reported on DNN model adaptation, very little has been done on LSTM model adaptation. In this paper we present our extensive studies of speaker adaptation of LSTM-RNN models for speech recognition. We investigated different adaptation methods combined with KL-divergence based regularization, where and which network component to adapt, supervised versus unsupervised adaptation and asymptotic analysis. We made a few distinct and important observations. In a large vocabulary speech recognition task, by adapting only 2.5 of the LSTM model parameters using 50 utterances per speaker, we obtained 12.6 WERR on the dev set and 9.1 WERR on the evaluation set over a strong LSTM baseline model. We propose the use of a hierarchical, two-stage discriminant transformation for obtaining audio-visual features that improve automatic speech recognition. Linear discriminant analysis (LDA), followed by a maximum likelihood linear transform (MLLT) is first applied to MFCC based audio-only features, as well as on visual only features, obtained by a discrete cosine transform of the video region of interest. Subsequently, a second stage of LDA and MLLT is applied to the concatenation of the resulting single modality features. The obtained audio-visual features are used to train a traditional HMM based speech recognizer. Experiments on the IBM ViaVoice sup TM audio-visual database demonstrate that the proposed feature fusion method improves speaker-independent, large vocabulary, continuous speech recognition (LVCSR) for both clean and noisy audio conditions considered. A 24 relative word error rate reduction over an audio-only system is achieved in the latter case. Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs). This paper describes further optimizations of LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization, multi-frame inference, and robust inference using an -contaminated Gaussian loss function. Experimental results in subjective listening tests show that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based SPSS in runtime speed while maintaining naturalness. Evaluations between LSTM-RNN- based SPSS and HMM-driven unit selection speech synthesis are also presented.