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• A novel approach for single channel speech enhancement is presented, using the colored spectrograms. • Using deep neural network for spectrogram denoising and regression neural network for translating the colors of denoised spectrograms to short time Fouier transform (STFT) magnitudes. • Output is comparable to the state-of-the-art baseline models at a much reduced computational cost. Speech enhancement concerns the processes required to remove unwanted background sounds from the target speech to improve its quality and intelligibility. In this paper, a novel approach for single-channel speech enhancement is presented using colored spectrograms. We propose the use of a deep neural network (DNN) architecture adapted from the pix2pix generative adversarial network (GAN) and train it over colored spectrograms of speech to denoise them. After denoising, the colors of spectrograms are translated to magnitudes of short-time Fourier transform (STFT) using a shallow regression neural network. These estimated STFT magnitudes are later combined with the noisy phases to obtain an enhanced speech. The results show an improvement of almost 0.84 points in the perceptual evaluation of speech quality (PESQ) and 1 % in the short-term objective intelligibility (STOI) over the unprocessed noisy data. The gain in quality and intelligibility over the unprocessed signal is almost equal to the gain achieved by the baseline methods used for comparison with the proposed model, but at a much reduced computational cost. The proposed solution offers a comparative PESQ score at almost 10 times reduced computational cost than a similar baseline model that has generated the highest PESQ score trained on grayscaled spectrograms, while it provides only a 1 % deficit in STOI at 28 times reduced computational cost when compared to another baseline system based on convolutional neural network-GAN (CNN-GAN) that produces the most intelligible speech. [ABSTRACT FROM AUTHOR]

This paper presents a new domain compensation framework by using phonetically discriminative features which are extracted from domain-dependent deep neural networks (DNNs). The domain compensation can be applied in both unsupervised and supervised manner, depending on whether the domain information of the development data is provided or not in advance. In supervised manner, the DNNs are trained on the development speech recordings of each given domain separately. While in the unsupervised manner, the development datasets are first automatically clustered into different domains, by using the Gaussian Mixture Model mean supervectors which are generated from each of the speech recordings, DNNs are then trained on the resulting clusters. Finally, we compensate the domain variabilities during the target speaker modeling step using support vector machines, by feeding in statistical vectors which are derived from the discriminative features extracted from the domain-dependent DNNs. The main strength of our proposed framework is that it does not need any speaker labels in the development dataset, which makes the proposed framework of great advantage over the state-of-the-art techniques that need speaker labels to train inter-speaker and/or intra-speaker variability models or channel compensation. Three speaker verification systems are investigated to examine the effectiveness of this new framework. Experimental results on the NIST SRE 2010 task demonstrate competitive performances to the state-of-the-art techniques in an initial implementation of the proposed framework. [ABSTRACT FROM AUTHOR]

• In this work, we conduct Evaluations on large vocabulary continuous speech recognition (LVCSR) for children, to: • Compare older GMM-HMM models and newer DNN models. • Investigate different transfer learning adaptation techniques. • Assess effectiveness of different speaker normalization and adaptation techniques like VTLN, fMLLR, i-vector based adaptation versus the employed transfer learning technique. • Further, we conduct Analysis over the following parameters in context of transfer learning: • DNN model parameters. • Amount of adaptation data. • Effect of children's ages. • Age dependent transformations obtained from transfer learning and their validity, portability over the children's age span. • We finally provide Recommendations on: • Favorable transfer learning adaptation strategies for low data and high data scenarios. • Suggested transfer learning adaptation techniques for children of different ages. • Amount of adaptation data required for efficient performance over children's ages. • Potential future research directions and relevant challenges and problems persisting in children speech recognition. Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient modeling. This work attempts to address the key challenges using transfer learning from adult's models to children's models in a Deep Neural Network (DNN) framework for children's Automatic Speech Recognition (ASR) task evaluating on multiple children's speech corpora with a large vocabulary. The paper presents a systematic and an extensive analysis of the proposed transfer learning technique considering the key factors affecting children's speech recognition from prior literature. Evaluations are presented on (i) comparisons of earlier GMM-HMM and the newer DNN Models, (ii) effectiveness of standard adaptation techniques versus transfer learning, (iii) various adaptation configurations in tackling the variabilities present in children speech, in terms of (a) acoustic spectral variability, and (b) pronunciation variability and linguistic constraints. Our Analysis spans over (i) number of DNN model parameters (for adaptation), (ii) amount of adaptation data, (iii) ages of children, (iv) age dependent-independent adaptation. Finally, we provide Recommendations on (i) the favorable strategies over various aforementioned - analyzed parameters, and (ii) potential future research directions and relevant challenges/problems persisting in DNN based ASR for children's speech. [ABSTRACT FROM AUTHOR]