This paper considers the generation of feedback utterances for speaking skills training of non-native English learners. The proposed feedback is in the form of a combination of the learner's voice and the linguistic gestures, i.e., the prosody or pronunciation, of a native speaker. Both accent reduction method and voice conversion method are employed to generate feedback stimuli. For accent reduction, three speech synthesis methods, namely pitch-synchronous overlap and add (PSOLA), harmonic stochastic model (HSM), and speech transformation and representation by adaptive interpolation of weighted spectrogram (STRAIGHT) are used to reduce the accent of the utterances of English learners. For voice conversion, the teacher's voice is converted to that of the learner and the converted speech is used as a feedback. Objective measurements are employed to assess the nativeness and acoustic quality of the generated stimuli. A feedback scheme which combines the accent reduction and voice conversion methods is also proposed.

This paper focuses on the generation of accurate phonetic segmentations. Statistical methods based on absolute and relative correction are discussed and experimented on both monophone and biphone models to improve the segmentation results. The influence of search range on the statistical correction process is studied and a state selection technique is used to enhance the correction results. This paper also explores the influence of resolution (stepsize) of HMMs and proposes a multi-resolution fusion process to further refine the statistically corrected results. Improvements of segmentation results in terms of segmentation accuracy, mean absolute error (MAE), and root mean square error (RMSE) can be observed by applying the proposed refinement methods.

In this paper, the reconstruction of the feedback polynomial as well as the initial state of a linear feedback shift register (LFSR) in a synchronous scrambler placed after a channel encoder is studied. The study is first based on the assumption that the channel is noiseless and then extended to the noisy channel condition. The dual words, which are orthogonal to the codewords generated by the channel encoder, are used in the reconstruction algorithm. The number of bits required by the new algorithm is compared with another recently proposed algorithm and results show that the number of bits required to do the reconstruction can be significantly reduced.

It is well known that in order to build linear scramblers and stream ciphers that are robust against correlation-based reconstruction, primitive polynomials which do not have sparse multiples of moderate degrees must be used. In this paper, the existence and density of such ‘good primitive polynomials’ are studied. Two theoretical lower bounds on the degree d of the primitive polynomial are derived. When d is larger than the first lower bound, there exists at least one primitive polynomial of degree d which does not have any sparse multiple of moderate degree and when d is larger than the second lower bound, it is almost guaranteed that a randomly chosen primitive polynomial of degree d does not have any sparse multiples of moderate degree. To make the lower bound tight, the distribution of the minimum degrees of sparse multiples of primitive polynomials is investigated in this paper. From comparison, it can be seen that the lower bounds obtained in this paper are much better than the previous results reported in the literature.

In this paper, the problem of reconstruction of the feedback polynomial in a linear scrambler is studied. Our work contains two parts. In the first part, schemes to improve the performance of an existing reconstruction algorithm are proposed. Simulation results show that both the detection capability and speed of the existing algorithm are significantly improved by using our proposed schemes. In the second part, the reconstruction of linear scramblers in the presence of channel noise is investigated. We consider flipped bits due to noise as well as insertion of bits in the scrambled bit sequence. For both cases, factors which affect the performance of the reconstruction algorithm are discussed.

The existing algorithm for reconstruction of the feedback polynomial in a linear scrambler relies on the assumption that the input sequence is produced by a biased memoryless source and the source bias is known and used for reconstruction. In this paper, the problem of reconstruction of the feedback polynomial in a linear scrambler without knowledge of the source bias is studied. An algorithm is proposed to reconstruct the scrambler without knowledge of the source bias and factors which affect the performance of this algorithm are discussed. A scheme which reduces the number of bits used in the reconstruction without affecting the detection capability of the reconstruction algorithm is also proposed in this paper.

A linear feedback shift register (LFSR) is a basic component of a linear scrambler and a stream cipher for a communication system. And primitive polynomials are used as the feedback polynomials of the LFSRs. In a non-cooperative context, the reverse-engineering of a linear scrambler and a stream cipher includes recovering the feedback polynomials and the LFSR's initial states (which are the secret keys in the case of stream ciphers). The problem of recovering the secret keys of stream ciphers has been extensively studied. For example, an effective approach for recovering a secret key is known as the correlation attack in the literature. The problem of reconstructing the feedback polynomials of a stream cipher and a linear scrambler has been studied recently. Both recovering the LFSR initial states by the above-mentioned correlation attack and reconstructing the feedback polynomials are highly dependent on an assumption, that is, they require that the feedback polynomials have sparse multiples of moderate degrees. Hence, in order to build linear scramblers and stream ciphers that are robust against reverse engineering, we should use primitive polynomials which do not have sparse multiples of moderate degrees. In this paper, we study the existence of primitive polynomials which do not have sparse multiples of moderate degrees, and the density of such primitive polynomials among all primitive polynomials. Our results on the existence and density of such primitive polynomials are better than the previous results in the literature.

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well knownk-means or fuzzyc-means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by usingk-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.

Despite the success of recent speech enhancement algorithms, the enhanced signals still suffer from undesirable speech distortion caused by over-attenuation of weak speech spectral components. In this paper, a post-processing technique based on the regeneration of both voiced and unvoiced speech is proposed to alleviate this problem. A non-linear transformation is first applied to aWiener filtered speech and the transformed signal is multiplied by a pre-estimated spectral envelop to form the regenerated speech. The resulting speech is then obtained using a weighted combination of the regenerated speech components and the filtered speech. This process significantly improves the resulting speech quality as compared to the original filtered version. It results in speech that sounds less lowpassed. Also, the residual musical noise is significantly masked by the regenerated speech components. Objective measures show that the quality of the resulting speech is much closer to the clean speech as compared to the original Wiener filtered speech.

The paper addresses a bitstream scalable coder based on the MPEG-4 scalable lossless (SLS) coding system where, in contrast to SLS, the bitrate of the enhancement layer is not fixed but instead an attempt is made to create a quality-fixed enhancement layer. With a PCM audio input, the proposed structure is able to produce an audio version with near-transparent quality on top of the existing low-quality version. In particular, the proposed fixed quality enhancing process with checking procedures is able to provide the minimum amount of enhancement for the low-quality version to obtain a near-transparent quality that is almost indistinguishable from the CD quality. In addition, a bitrate estimation model is proposed. The model enables the direct estimation of the enhancing bitrate from two parameters extracted from the encoding process of the low-quality version. Evaluation results indicate that a better defined quality level is guaranteed compared to a fixed bitrate setting and that in the mean a lower (approximately 20%) bitrate is attained. It is also shown that the estimation model proposed is able to accurately predict the necessary enhancing bitrate and at the same time, reduce the complexity by around 17%.

A fully scalable audio coding structure based on a novel combination of the non-core MPEG-4 scalable lossless audio coding (SLS), the state-of-the-art psychoacoustic model, joint stereo coding and the perceptually prioritized bit-plane coding is presented in this paper. The psychoacoustic information is implicitly embedded in the scalable bitstream with negligible amount of side information and trivial modification to the standardized SLS decoder. Results of extensive evaluation show that the subjective quality of scalable audio is improved significantly.

The bit allocation algorithm for stereo channels in MPEG-4 scalable lossless coding (SLS) is not optimized. A perceptually enhanced stereo bit allocation algorithm for fully scalable audio coding is presented in this paper. According to the energy distribution in different channels, the bitrate is allocated in a much more efficient manner. Experiment results show that the proposed method significantly improves the perceptual quality of the fully scalable audio at various bitrates without introducing any new side information.

A perceptually enhanced prioritized bit-plane audio coding algorithm is presented in this paper. According to the energy distribution in different frequency regions, the bit-planes are prioritized with optimized parameters. Based on the statistical modeling of the frequency spectrum, a much more simplified implementation of prioritized bit-plane coding is integrated with the recent release of MPEG-4 scalable lossless (SLS) audio coding structure by replacing the sequential bit-plane coding in the enhancement layer. With zero extra side information, trivial added complexity, and modification to the original SLS structure, extensive experimental results show that the perceptual quality of SLS with noncore and very low core bit-rate is improved significantly in a wide range of bit-rate combinations. Fully scalable audio coding up to lossless with much enhanced perceptual quality is thus achieved.

In this paper, autoregressive parameter estimation for Kalman filtering speech enhancement is studied. In conventional Kalman filtering speech enhancement, spectral subtraction is usually used for speech autoregressive (AR) parameter estimation. We propose log spectral amplitude (LSA) minimum mean-square error (MMSE) instead of spectral subtraction for the estimation of speech AR parameters. Based on an observation that full-band Kalman filtering speech enhancement often causes an unbalanced noise reduction between speech and non-speech segments, a spectral solution is proposed to overcome the unbalanced reduction of noise. This is done by shaping the spectral envelopes of the noise through likelihood ratio. Our simulation results show the effectiveness of the proposed method.

MPEG-4 scalable lossless (SLS) coding is a unified solution for demands in high compression perceptual audio and high quality lossless audio. It provides a fine-grain scalable extension to the MPEG-4 advanced audio coding (AAC) perceptual audio coder up to fully lossless reconstruction. It is observed that the quality of SLS coded audio is still far from optimal when the core bitrate is low. With this observation, an adaptive bit-plane scanning (ABPS) method is proposed in this paper. In ABPS, the bit-plane scanning order is adaptive to the energy distribution of the signal to be coded. The results show that with ABPS, the perceptual quality of the audio under low core bitrate scenario is significantly improved without any extra payload.

MPEG-4 Scalable Lossless (SLS) coding is the latest released ISO international standard for scalable audio coding. Besides its function as an extension of MPEG-4 Advanced Audio Coding (AAC) perceptual audio coder, SLS has a "non-core mode" that is able to offer full scalability. The perceptual audio coder is absent in this mode and scalability is achieved through pure bit-plane coding. In this paper, a perceptually enhanced bit-plane coding method, namely Quad-level Bit-Plane Coding (QBPC) is proposed to enhance the perceptual quality of fully scalable audio at intermediate bitrates. With QBPC structure, the perceptual quality of fully scalable audio coded by SLS is significantly improved in a wide range of intermediate bitrates. Meanwhile this is achieved with trivial added overhead and complexity.

In this paper, we derive a relation for the circular convolution operation in the discrete sine and cosine transform domains. The transform coefficients are either symmetric or asymmetric and hence we need to calculate only half of the total coefficients. Since fast algorithms are available for the computation of discrete sine and cosine transforms, the proposed method is an alternative to the discrete Fourier transform method for filtering applications.

In MPEG-4 scalable lossless coding (SLS) which was recently published as an ISO standard in June 2006, the integer modified discrete cosine transform (IntMDCT) was adopted to enable efficient lossless reconstruction. In addition, there is an MDCT filterbank which is inherent to the advanced audio coding (AAC) core that is present in the SLS codec. The presence of two filterbanks have undoubtedly increased the complexity of the implementation, and it is for this reason that the MDCT is disabled and the IntMDCT is then the only type of filterbank that is employed in SLS for both lossy and lossless operations. Because of the rounding operations in the IntMDCT, there is a concern if the use of IntMDCT for perceptual audio coding will eventually degrade the fidelity of the audio codec. This paper addresses this concern by analyzing the performance of the IntMDCT in a lossy coding scenario. It is found that noise introduced by the IntMDCT does not affect the perceptual quality of the coded audio under standard playback circumstances. As such, it concludes that the MDCT and IntMDCT filterbanks are interchangeable at lossy bitrate, and the way of using only the IntMDCT filterbank in scalable audio coding is also justified.

A signal subspace scheme based on masking properties is proposed for enhancement of speech degraded by additive noise. Since the masking properties are related to the critical frequency band that is derived from the characteristics of human cochlea, the incorporation of masking threshold into a subspace technique requires the transformation between the frequency and eigen domains. We present and apply an invertible transformation between the frequency and eigen domains. In this paper, we use masking properties of the human auditory system to define the audible noise quantity in the eigendomain. We derive the eigen-decomposition of the estimated speech autocorrelation matrix with the assumption of white noise. Subsequently, an audible noise reduction scheme is developed based on a signal subspace technique, and the implementation of our proposed scheme is outlined. We further extend the scheme to the colored noise case. Simulation results show the superiority of our proposed scheme over other existing subspace methods in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), modified Bark spectral distortion (MBSD), spectrogram and informal listening tests.

A simplified parallel step-by-step decoding algorithm is proposed for decoding Reed-Solomon (RS) codes. It uses new method to calculate the determinants of the temporarily changed syndrome matrices, based on the property of these matrices determined in this paper. By using the proposed method, the calculations of the determinants of the temporarily changed syndrome matrices become much simpler and thus the computational complexity of the step-by-step decoding algorithm is significantly reduced.

To enhance the noisy speech signal, a perceptual weighting based Kalman filtering is investigated. Our study is to seek a high perceptual quality of speech enhancement system which optimizes the trade-off between the speech distortion and noise reduction. Using perceptual weighting to replace the masking threshold avoids the frequency domain complexity, it is suitable for time domain Kalman filtering to estimate the state-space vector in only time domain. Through many simulations, it is demonstrated that the proposed perceptual Kalman filtering outperforms the conventional Kalman filtering.

Wide bitrate range scalability is now the latest trend in audio coding. A lot of efforts has been devoted to the development of algorithms for more efficient scalable audio coder that scales from very low bitrate. Scalable audio coding technique such as MPEG-4 scalable lossless coding (SLS) offers a unified solution for high-compression perceptual audio and high-quality lossless audio. SLS provides a fine-grain scalable extension of the well-known MPEG-4 advanced audio coding (AAC) perceptual audio coder up to fully lossless reconstruction. Recently, the combination of SLS and AAC coder is renamed as "high definition advanced audio coding" (HD-AAC). It is observed that HD-AAC can be further improved at intermediate enhancement bitrate when the core bitrate is low. In this paper, a perceptually prioritized bit-plane coding (PPBPC) is proposed. With this novel coding scheme, the bit-plane coding is performed with priorities according to the perceptual information of the signal to be coded. By using this low-complexity structure with trivial extra side information, the bit-plane coding for scalable audio can be implemented in a perceptually more efficient manner and the quality of the audio under aforementioned scenario is greatly improved

A novel subspace-based speech enhancement scheme, based on a criterion of audible noise reduction, is considered. The masking properties of the human auditory system are used to define the audible noise quantity in the eigen-domain. Subsequently, an audible noise reduction scheme is developed based on a signal subspace technique. We derive the eigendecomposition of the estimated speech autocorrelation matrix with the assumption of white noise and outline the implementation of our proposed scheme. We further extend the scheme to the colored noise case. Simulation results show that our proposed scheme outperforms many existing subspace methods in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ) and informal listening tests.

With the proliferation of broadband access and continuous decline of storage prize per gigabyte, there has been an increasing demand of audio solution that provides high sampling rate and high resolution. Lossless audio is undoubtedly the ultimate solution. In response to this demand, MPEG issued a call for proposal soliciting technology contributions that provides a state-of-art solution. At the technology end, lossless compression requires the usage of integer transform. The integer modified discrete cosine transform (IntMDCT) has been adopted in MPEG-4 scalable to lossless (SLS) coding to enable this efficient lossless operation. Because of rounding operations, rounding errors introduced by IntMDCT exist during the whole coding process. With the SLS having capability of using operations that spreads over the bitrate spectrum which ranges from lossy to lossless, it is of interest to study the effect of rounding errors in IntMDCT for operation of SLS in lossy mode. This paper analyzes the contributions of noise due to these errors. It is found that the noise introduced by rounding operations of IntMDCT does not affect the perceptual quality of the coded audio under any circumstances. As such, it concludes that the MDCT and IntMDCT filterbanks are interchangeable at lossy bitrate. With the fact that SLS uses both MDCT and IntMDCT, the finding in this paper suggests the possibility of using only IntMDCT filterbank.

Masking properties have been widely exploited in speech enhancement techniques, especially those implemented in the spectral domain. The incorporation of auditory masking in a subspace technique invariably requires a transformation linking the frequency and eigendomains. In this letter, an invertible transformation between the frequency and eigendomains is derived. The proposed transformation is verified through a conventional masking-based subspace speech-enhancement method. Simulation results show that our proposed transformation for speech enhancement outperforms the conventional transformation in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), and listening tests.

This paper proposes /spl beta/-order minimum mean-square error (MMSE) speech enhancement approach for estimating the short time spectral amplitude (STSA) of a speech signal. We analyze the characteristics of the /spl beta/-order STSA MMSE estimator and the relation between the value of /spl beta/ and the spectral amplitude gain function of the MMSE method. We further investigate the effectiveness of a range of fixed-/spl beta/ values in estimating STSA based on the MMSE criterion, and discuss how the /spl beta/ value could be adapted using the frame signal-to-noise ratio (SNR). The performance of the proposed speech enhancement approach is then evaluated through spectrogram inspection, objective speech distortion measures and subjective listening tests using several types of noise sources from the NOISEX-92 database. Evaluation results show that our approach can achieve a more significant noise reduction and a better spectral estimation of weak speech spectral components from a noisy signal as compared to many existing speech enhancement algorithms.

The paper describes a new speech enhancement approach which employs adaptive /spl beta/-order minimum mean square error (MMSE) spectral estimation of the short time spectral amplitude (STSA) of a speech signal. In the proposed approach, the human perceptual auditory masking effect is incorporated into the speech enhancement algorithm. The relationship between the value of /spl beta/ and the noise masking threshold is considered. The algorithm is based on a criterion by which the audible noise may be masked rather than being attenuated, thereby reducing the chance of speech distortion. Performance assessment is given to show that our proposal can achieve a more significant noise reduction and a better spectral estimation of weak speech spectral components from a noisy signal as compared to many existing speech enhancement algorithms.

A single channel speech enhancement system based on Kalman filtering and masking properties of the human auditory system is studied. The main objective of our study is to develop a high quality speech enhancement system for car mobile communication, which optimizes the tradeoff between speech distortion and noise reduction. To develop a robust Kalman filtering speech enhancement system, we propose that the noise variance estimate be modified as a function of masking threshold in each subband. By using such a Kalman filtering with masking method, we can achieve better results as compared to full band Kalman filtering, especially in the case of weak speech spectral components in noise. Through a great deal of simulations, we show the advantages of our proposed system in terms of objective and subjective measurements as compared to many existing speech enhancement methods.

This paper introduces an adaptive /spl beta/-order minimum mean square error (MMSE) spectral estimator for the short time spectral amplitude (STSA) of speech. The characteristic of /spl beta/-order MMSE attenuation function is introduced and analyzed. The performance of the proposed adaptive /spl beta/-order MMSE has been thoroughly examined by a large number of computer simulations. The new proposed scheme has been found to outperform the conventional a priori SNR Wiener filtering, the Ephraim and Malah (1984) STSA-MMSE and log spectral amplitude (LSA) schemes. It can achieve a more significant noise reduction and a better spectral estimation for weak speech components from a noisy speech signal as compared to the conventional schemes.

This paper presents an innovative way of using the two-dimensional (2-D) Fourier transform for speech enhancement. The blocking and windowing of the speech data for the 2-D Fourier transform are explained in detail. Several techniques of filtering in the 2-D Fourier transform domain are also proposed. They include magnitude spectral subtraction, 2-D Wiener filtering as well as a hybrid filter which effectively combines the one-dimensional (1-D) Wiener filter with the 2-D Wiener filter. The proposed hybrid filter compares favorably against other techniques using an objective test.

This paper presents our study on the feasibility and effectiveness of using the MELP (mixed excitation linear prediction) model for coding wideband (7 kHz) speech signals at a transmission bit rate of 8 kbps. In order to achieve a reasonably good subjective quality for the decoded speech while maintaining a low operating bit rate at the same time, modifications to the pitch estimation, LP analysis/synthesis and post filtering stages of the original MELP model are discussed. Informal listening tests show that the subjective quality of the decoded speech of the proposed coder is rated to be slightly better than the MPEG-4 CELP coder operating at 14.4 kbps for both male and female utterances. The subjective quality of the decoded female utterances from the proposed coder operating at 8.4 kbps is rated to be comparable to that produced by the ITU G.722 coder operating at 48 kbps.