Time–Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition

The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time-frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexity. The BDHLDA method is finally extended to the GMM domain, using the simpler TFC features during re-estimation to provide significantly improved computation speed. Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches

Two-Dimensional Heteroscedastic Feature Extraction Technique for Face Recognition

One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with heteroscedastic data. In this paper, we present a novel two-dimensional feature extraction technique for face recognition which is capable of handling the heteroscedastic data in the dataset. The technique is a general form of two-dimensional linear discriminant analysis. It generalizes the interclass scatter matrix of two-dimensional LDA by applying the Chernoff distance as a measure of separation of every pair of clusters with the same index in different classes. By employing the new distance, our method can capture the discriminatory information presented in the difference of covariance matrices of different clusters in the datasets while preserving the computational simplicity of eigenvalue-based techniques. So our approach is a proper technique for high-dimensional applications such as face recognition. Experimental results on CMU-PIE, AR and AT & T face databases demonstrate the effectiveness of our method in term of classification accuracy

Impact of single-microphone dereverberation on DNN-based meeting transcription systems

Over the past few decades, a range of front-end techniques have been proposed to improve the robustness of automatic speech recognition systems against environmental distortion. While these techniques are effective for small tasks consisting of carefully designed data sets, especially when used with a classical acoustic model, there has been limited evidence that they are useful for a state-of-the-art system with large scale realistic data. This paper focuses on reverberation as a type of distortion and investigates the degree to which dereverberation processing can improve the performance of various forms of acoustic models based on deep neural networks (DNNs) in a challenging meeting transcription task using a single distant microphone. Experimental results show that dereverberation improves the recognition performance regardless of the acoustic model structure and the type of the feature vectors input into the neural networks, providing additional relative improvements of 4.7% and 4.1% to our best configured speaker-independent and speaker-adaptive DNN-based systems, respectively.Xie Chen was funded by Toshiba Research Europe Ltd, Cambridge Research Lab.This is the accepted manuscript of a paper published in the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, Issue Date: 4-9 May 2014, Written by: Yoshioka, T.; Xie Chen; Gales, M.J.F.)

Speech Recognition Using Augmented Conditional Random Fields

Acoustic modeling based on hidden Markov models (HMMs) is employed by state-of-the-art stochastic speech recognition systems. Although HMMs are a natural choice to warp the time axis and model the temporal phenomena in the speech signal, their conditional independence properties limit their ability to model spectral phenomena well. In this paper, a new acoustic modeling paradigm based on augmented conditional random fields (ACRFs) is investigated and developed. This paradigm addresses some limitations of HMMs while maintaining many of the aspects which have made them successful. In particular, the acoustic modeling problem is reformulated in a data driven, sparse, augmented space to increase discrimination. Acoustic context modeling is explicitly integrated to handle the sequential phenomena of the speech signal. We present an efficient framework for estimating these models that ensures scalability and generality. In the TIMIT phone recognition task, a phone error rate of 23.0\% was recorded on the full test set, a significant improvement over comparable HMM-based systems

Pitch adaptive features for LVCSR

We have investigated the use of a pitch adaptive spectral representation on large vocabulary speech recognition, in conjunction with speaker normalisation techniques. We have compared the effect of a smoothed spectrogram to the pitch adaptive spectral analysis by decoupling these two components of STRAIGHT. Experiments performed on a large vocabulary meeting speech recognition task highlight the importance of combining a pitch adaptive spectral representation with a conventional fixed window spectral analysis. We found evidence that STRAIGHT pitch adaptive features are more speaker independent than conventional MFCCs without pitch adaptation, thus they also provide better performances when combined using feature combination techniques such as Heteroscedastic Linear Discriminant Analysis

Expressive Speech Identifications based on Hidden Markov Model

This paper concerns a sub-area of a larger research field of Affective Computing, focusing on the employment of affect-recognition systems using speech modality. It is proposed that speech-based affect identification systems could play an important role as next generation biometric identification systems that are aimed at determining a person’s ‘state of mind’, or psycho-physiological state. The possible areas for the deployment of voice-affect recognition technology are discussed. Additionally, the experiments and results for emotion identification in speech based on a Hidden Markov Models (HMMs) classifier are also presented. The result from experiment suggests that certain speech feature is more precise to identify certain emotional state, and that happiness is the most difficult emotion to detect

Combining Spectral Representations for Large Vocabulary Continuous Speech Recognition

In this paper we investigate the combination of complementary acoustic feature streams in large vocabulary continuous speech recognition (LVCSR). We have explored the use of acoustic features obtained using a pitch-synchronous analysis, STRAIGHT, in combination with conventional features such as mel frequency cepstral coefficients. Pitch-synchronous acoustic features are of particular interest when used with vocal tract length normalisation (VTLN) which is known to be affected by the fundamental frequency. We have combined these spectral representations directly at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA) and at the system level using ROVER. We evaluated this approach on three LVCSR tasks: dictated newspaper text (WSJCAM0), conversational telephone speech (CTS), and multiparty meeting transcription. The CTS and meeting transcription experiments were both evaluated using standard NIST test sets and evaluation protocols. Our results indicate that combining conventional and pitch-synchronous acoustic feature sets using HLDA results in a consistent, significant decrease in word error rate across all three tasks. Combining at the system level using ROVER resulted in a further significant decrease in word error rate