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This paper proposes a novel active learning method to save annotation effort when preparing material to train sound event classifiers. K-medoids clustering is performed on unlabeled sound segments, and medoids of clusters are presented to annotators for labeling. The annotated label for a medoid is used to derive predicted labels for other cluster members. The obtained labels are used to build a classifier...
In this paper, we present an investigation on technical details of the byte-level convolutional layer which replaces the conventional linear word projection layer in the neural language model. In particular, we discuss and compare the effective filter configurations, pooling types and the use of bytes instead of characters. We carry out experiments on language packs released by the IARPA Babel project...
This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes. These scenes are used to build a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls' absorption coefficients...
The aim of this work is the estimation of respiratory flow from lung sound recordings, i.e. acoustic airflow estimation. With a 16-channel lung sound recording device, we simultaneously record the respiratory flow and the lung sounds on the posterior chest from six lung-healthy subjects in supine position. For the recordings of four selected sensor positions, we extract linear frequency cepstral coefficient...
Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met.
Ecologists can assess the health of flooded habitats or wetlands by studying the variations in the populations of bioindicators such as anurans (i.e., frogs and toads). To monitor anuran populations, ecologists manually identify anuran species from audio recordings. This identification task can be significantly streamlined by the availability of an automated method for anuran identification. Previous...
Recent experiments show that deep bidirectional long short-term memory (BLSTM) recurrent neural network acoustic models outperform feedforward neural networks for automatic speech recognition (ASR). However, their training requires a lot of tuning and experience. In this work, we provide a comprehensive overview over various BLSTM training aspects and their interplay within ASR, which has been missing...
Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve...
Deep neural networks (DNN) have achieved significant success in the field of speech recognition. One of the main advantages of the DNN is automatic feature extraction without human intervention. Therefore, we incorporate a pseudo-filterbank layer to the bottom of DNN and train the whole filterbank layer and the following networks jointly, while most systems take pre-defined mel-scale filterbanks as...
This paper presents a method to extract structural spectral features from spectral envelopes using what-where autoencoders (WWAE) for statistical parametric speech synthesis (SPSS). A WWAE is constructed by concatenating a convolutional net for input encoding and a deconvolutional net for reconstruction. The output values of the max-pooling layer in the encoder and the positions of the max-pooling...
When using connectionist temporal classification (CTC) based acoustic models (AMs) for large vocabulary continuous speech recognition (LVCSR), most previous studies have used a naive interpolation of the CTC-AM score and an additional language model score, although there is no theoretical justification for such an approach. On the other hand, we recently proposed a theoretically more sound decoding...
Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional...
Deep learning has significantly advanced state-of-the-art of speech recognition in the past few years. However, compared to conventional Gaussian mixture acoustic models, neural network models are usually much larger, and are therefore not very deployable in embedded devices. Previously, we investigated a compact highway deep neural network (HDNN) for acoustic modelling, which is a type of depth-gated...
Dropout, the random dropping out of activations according to a specified rate, is a very simple but effective method to avoid over-fitting of deep neural networks to the training data.
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first paper that performs an extensive empirical study on its effects in speech recognition. We view training as sampling from a distribution and show that these distributions...
In this paper, we target improving the accuracy of acoustic modelling for statistical parametric speech synthesis (SPSS) and introduce the convolutional neural network (CNN) due to its powerful capacity in locality modelling. A novel model architecture combining unidirectional long short-term memory (LSTM) and a time-domain convolutional output layer (COL) is proposed and employed to acoustic modelling...
Automatic and fast tagging of natural sounds in audio collections is a very challenging task due to wide acoustic variations, the large number of possible tags, the incomplete and ambiguous tags provided by different labellers. To handle these problems, we use a co-regularization approach to learn a pair of classifiers on sound and text. The first classifier maps low-level audio features to a true...
In this paper we present an extension of our previously described neural machine translation based system for punctuated transcription. This extension allows the system to map from per frame acoustic features to word level representations by replacing the traditional encoder in the encoder-decoder architecture with a hierarchical encoder. Furthermore, we show that a system combining lexical and acoustic...
We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages...
Proxy-word based out of vocabulary (OOV) keyword search has been proven to be quite effective in keyword search. In proxy-word based OOV keyword search, each OOV keyword is assigned several proxies and detections of the proxies are regarded as detections of the OOV keywords. However, the confidence scores of these detections are still those of the proxies from lattices. To obtain a better confidence...
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