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A common framework of identifying bird species from audio recordings involves detecting bird song segments, which will be subsequently input to a classifier. In-field recordings are contaminated with various environmental noise. For such recordings, supervised segmentation has been observed to outperform unsupervised energy-based approaches. Prior supervised segmentation work considers only pixel-level...
This paper presents an HMM-based synthesis approach for speechlaughs. The building stone of this project was the idea of the co-occurrence of smile and laughter bursts in varying proportions within amused speech utterances. A corpus with three complementary speaking styles was used to train the underlying HMM models: neutral speech, speech-smile, and finally laughter in different articulatory configurations...
This paper proposes novel models of F0 contours and phone durations using Gaussian process regression and classification (GPR and GPC) for statistical parametric speech synthesis. Although the use of frame-based GPR has shown the effectiveness of spectral feature modeling in previous studies, the application of GPR to prosodic features, i.e., F0 and phone duration, was not investigated sufficiently...
This paper investigates the incorporation of hidden Markov model (HMM) based emphatic speech synthesis for audio exaggeration into an audio-visual speech synthesis framework for the corrective feedback in computer-aided pronunciation training (CAPT). To improve the voice quality of the synthetic emphatic speech, this paper proposes a new method for HMM training. In this method, the contextual questions...
We propose a sparse hidden Markov model (HMM)-based single-channel speech enhancement method that models the speech and noise gains accurately in both stationary and nonstationary environments. The objective function is augmented with an lp regularization term resulting in a sparse autoregressive HMM (SARHMM). The method encourages sparsity in the speech- and noise- modeling, which eliminates the...
This paper proposes an improved time-frequency trajectory excitation (TFTE) modeling method for a statistical parametric speech synthesis system. The proposed approach overcomes the dimensional variation problem of the training process caused by the inherent nature of the pitch-dependent analysis paradigm. By reducing the redundancies of the parameters using predicted average block coefficients (PABC),...
We propose a representation of f0 using the Continuous Wavelet Transform (CWT) and the Discrete Cosine Transform (DCT). The CWT decomposes the signal into various scales of selected frequencies, while the DCT compactly represents complex contours as a weighted sum of cosine functions. The proposed approach has the advantage of combining signal decomposition and higher-level representations, thus modeling...
Sinusoidal vocoders can generate high quality speech, but they have not been extensively applied to statistical parametric speech synthesis. This paper presents two ways for using dynamic sinusoidal models for statistical speech synthesis, enabling the sinusoid parameters to be modelled in HMM-based synthesis. In the first method, features extracted from a fixed- and low-dimensional, perception-based...
In this paper we investigate the use of noise-robust features characterizing the speech excitation signal as complementary features to the usually considered vocal tract based features for Automatic Speech Recognition (ASR). The proposed Excitation-based Features (EBF) are tested in a state-of-the-art Deep Neural Network (DNN) based hybrid acoustic model for speech recognition. The suggested excitation...
In this paper we propose softSAD: the direct integration of speech posteriors into a speaker recognition system as an alternative to using speech activity detection (SAD). Motivated by the need to use audio from short recordings more efficiently, softSAD removes the need to discard audio using speech/non-speech decisions based on a threshold as done with SAD. Instead, softSAD explicitly integrates...
This paper introduces a new formulation of Joint Factor Analysis (JFA) for text-dependent speaker recognition based on left-to-right modeling with tied mixture HMMs. It accommodates many different ways of extracting multiple features to characterize speakers (features may or may not be HMM state-dependent, they may be modeled with subspace or factorial priors and these priors maybe imputed from text-dependent...
Acoustic models based on Gaussian mixture models (GMMs) typically use short span acoustic feature inputs. This does not capture long-term temporal information from speech owing to the conditional independence assumption of hidden Markov models. In this paper, we present an implicit approach that approximates the joint distribution of long span features by product of factorized models, in contrast...
Recently, we have proposed a general adaptation scheme for deep neural network based on discriminant condition codes and applied it to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion [1, 2, 3, 4]. In this case, each condition code is associated with one speaker in data, which is thus called...
Deep neural network (DNN) based speech recognizers have recently replaced Gaussian mixture (GMM) based systems as the state-of-the-art. HMM/DNN systems have kept many refinements of the HMM/GMM framework, even though some of these may be suboptimal for them. One such example is the creation of context-dependent tied states, for which an efficient decision tree state tying method exists. The tied states...
The deep neural network component of current hybrid speech recognizers is trained on a context of consecutive feature vectors. Here, we investigate whether the time span of this input can be extended by splitting it up and modeling it in smaller chunks. One method for this is to train a hierarchy of two networks, while the less well-known split temporal context (STC) method models the left and right...
A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to achieve better mixture density distribution. In previous work such a discriminative splitting approach was presented. Similarly, the resolution of a deep neural network may also be increased...
To solve the acoustic-to-articulatory inversion problem, this paper proposes a deep bidirectional long short term memory recurrent neural network and a deep recurrent mixture density network. The articulatory parameters of the current frame may have correlations with the acoustic features many frames before or after. The traditional pre-designed fixed-length context window may be either insufficient...
In this paper, the Kullback-Leibler Hidden Markov Model (KL-HMMs) is applied for unsupervised diarization of speech. A general approach to speaker diarization is to split the audio into uniform segments followed by one or more iterations of clustering of the segments and resegmentation of the audio. In the Information Bottlneck (IB) approach to diarization, short uniform segments are clustered using...
Based on the recently proposed speech pre-processing front-end with deep neural networks (DNNs), we first investigate different feature mapping directly from noisy speech via DNN for robust speech recognition. Next, we propose to jointly train a single DNN for both feature mapping and acoustic modeling. In the end, we show that the word error rate (WER) of the jointly trained system could be significantly...
This paper introduces a theory for max-product systems by analyzing them as discrete-time nonlinear dynamical systems that obey a superposition of a weighted maximum type and evolve on nonlinear spaces which we call complete weighted lattices. Special cases of such systems have found applications in speech recognition as weighted finite-state transducers and in belief propagation on graphical models...
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