Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.

Voice Activity Detection (VAD) is an important preprocessing step in any state-of-the-art speech recognition system. Choosing the right set of features and model architecture can be challenging and is an active area of research. In this paper we propose a novel approach to VAD to tackle both feature and model selection jointly. The proposed method is based on a CLDNN (Convolutional, Long Short-Term Memory, Deep Neural Networks) architecture fed directly with the raw waveform. We show that using the raw waveform allows the neural network to learn features directly for the task at hand, which is more powerful than using log-mel features, specially for noisy environments. In addition, using a CLDNN, which takes advantage of both frequency modeling with the CNN and temporal modeling with LSTM, is a much better model for VAD compared to the DNN. The proposed system achieves over 78% relative improvement in False Alarms (FA) at the operating point of 2% False Rejects (FR) on both clean and noisy conditions compared to a DNN of comparable size trained with log-mel features. In addition, we study the impact of the model size and the learned features to provide a better understanding of the proposed architecture.

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Language recognition systems based on bottleneck features have recently become the state-of-the-art in this research field, showing its success in the last Language Recognition Evaluation (LRE 2015) organized by NIST (U.S. National Institute of Standards and Technology). This type of system is based on a deep neural network (DNN) trained to discriminate between phonetic units, i.e. trained for the task of automatic speech recognition (ASR). This DNN aims to compress information in one of its layers, known as bottleneck (BN) layer, which is used to obtain a new frame representation of the audio signal. This representation has been proven to be useful for the task of language identification (LID). Thus, bottleneck features are used as input to the language recognition system, instead of a classical parameterization of the signal based on cepstral feature vectors such as MFCCs (Mel Frequency Cepstral Coefficients). Despite the success of this approach in language recognition, there is a lack of studies analyzing in a systematic way how the topology of the DNN influences the performance of bottleneck feature-based language recognition systems. In this work, we try to fill-in this gap, analyzing language recognition results with different topologies for the DNN used to extract the bottleneck features, comparing them and against a reference system based on a more classical cepstral representation of the input signal with a total variability model. This way, we obtain useful knowledge about how the DNN configuration influences bottleneck feature-based language recognition systems performance.