The purpose of a voice conversion (VC) system is to change the perceived speaker identity of a speech signal. We propose an algorithm based on converting the LPC spectrum and predicting the residual as a function of the target envelope parameters. We conduct listening tests based on speaker discrimination of same/difference pairs to measure the accuracy by which the converted voices match the desired target voices. To establish the level of human performance as a baseline, we first measure the ability of listeners to discriminate between original speech utterances under three conditions: normal, fundamental frequency and duration normalized, and LPC coded. Additionally, the spectral parameter conversion function is tested in isolation by listening to source, target, and converted speakers as LPC coded speech. The results show that the speaker identity of speech whose LPC spectrum has been converted can be recognized as the target speaker with the same level of performance as discriminating between LPC coded speech. However, the level of discrimination of converted utterances produced by the full VC system is significantly below that of speaker discrimination of natural speech.

Design and evaluation of a voice conversion algorithm based on spectral envelope mapping and residual prediction

A voice signal contains the psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word uttered by different speakers can be very different. In this paper, two theories are developed by considering two optimization criteria applied to both the training set and the test set. The first theory is well known and uses what is called Criterion 1 here and ends up with the average of all vectors belonging to the words in the training set. The second theory is a novel approach and uses what is called Criterion 2 here, and it is used to extract the common properties of all vectors belonging to the words in the training set. It is shown that Criterion 2 is superior to Criterion 1 when the training set is of concern. In Criterion 2, the individual differences are obtained by subtracting a reference vector from other vectors, and individual difference vectors are used to obtain orthogonal vector basis by using the Gram-Schmidt orthogonalization method. The common vector is obtained by subtracting projections of any vector of the training set on the orthogonal vectors from this same vector. It is proved that this common vector is unique for any word class in the training set and independent of the chosen reference vector. This common vector is used in isolated word recognition, and it is also shown that Criterion 2 is superior to Criterion 1 for the test set. From the theoretical and experimental study, it is seen that the recognition rates increase as the number of speakers in the training set increases. This means that the common vector obtained from Criterion 2 represents the common properties of a spoken word better than the common or average vector obtained from Criterion 1.

A novel approach to isolated word recognition

Voice conversion by mapping the speaker-specific features using pitch synchronous approach

This paper presents several improvements to our voice conversion system which we refer to as speaker transformation algorithm using segmental codebooks (STASC). First, a new concept, sentence HMM, is introduced for the alignment of speech waveforms sharing the same text. This alignment technique allows reliable and high resolution mapping between two speech waveforms. In addition, it is observed that energy and speaking rate differences between two speakers are not constant across all phonemes. Therefore a codebook based duration and energy scaling algorithm is proposed. Finally, a more detailed pitch modification is introduced that takes into account pitch range differences between source and target speakers in addition to mean pitch level differences. The proposed changes made a significant impact on the quality of transformed speech. Subjective listening tests showed that intelligibility is maintained at the same level as natural speech after the speaker transformation.

Speaker transformation using sentence HMM based alignments and detailed prosody modification

In this Modern era, image transmission and processing plays a major role. It would not be possible to retrieve information from satellite and medical images without the help of Image processing techniques. Image edge Enhancement is the art of examining images for identifying objects and judging their significance. The proposed work uses the concept of Artificial Bee Colony Algorithm which proved to be the most powerful unbiased optimization technique for sampling a large solution space. Because of its unbiased stochastic sampling, it was quickly adapted in image processing and thus for image edge enhancement as well. This paper deals with the techniques that help in improvising the quality of the image edges and in solving various complex image processing tasks such as segmentation, feature extraction, classification and image generation. The edge enhancement is done using hybridized smoothening filters by The Artificial Bee Colony optimization algorithm and compared it with the genetic algorithm.

A novel approach to image edge enhancement using Artificial Bee Colony optimization algorithm for hybridized smoothening filters

Provided are a speech restoration system and method for concealing packet losses. The system includes a demultiplexer that demultiplexes an input bit stream and divides the input bit stream into several packets; a packet loss concealing unit that produces and outputs a linear spectrum pair (LSP) coefficient representing the vocal tract of voice and an excitation signal corresponding to a lost frame, when a packet loss occurs; and a speech restoring unit that synthesizes voice using the packets input from the demultiplexer, outputs the result as restored voice, and synthesizes voice corresponding to a lost packet using the LSP coefficient and the excitation signal input from the packet loss concealing unit and outputs the result as restored voice when the lost packet is detected, wherein the packet loss concealing unit repeats linear prediction coefficients (LPCs) of a last-received valid frame, produces a first excitation signal for the lost frame using a time scale modification (TSM) method, when the lost frame is voiceless, and produces a second excitation signal by re-estimating a gain parameter based on the first excitation signal, when the lost frame is voiced.

Speech restoration system and method for concealing packet losses

Voice personality transformation using an orthogonal vector space conversion.

A newly developed image enhancement algorithm is described in this contribution. The proposed algorithm makes use of the signal subspace method to enhance images corrupted by uncorrelated additive noise. This enhancement is performed by eliminating the noise subspace and estimating clean image from the remaining signal subspace. We propose the block-adaptive Wiener filtering which engages properties of the human visual system to estimate clean image. This criterion enables one to not only preserve the detailed structure of the given image, but to reduce the level of background noise as well. Subjective evaluation tests show the superiority of the method proposed here. In particular, edge blurring effects are noticeably reduced compared to the conventional methods.

Image enhancement based on signal subspace approach

A transcoder for use between speech codecs using different Code-Excited Linear Prediction (CELP) type and a method therefor are disclosed. The transcoder includes a decoding unit of an input CELP codec, a transcoding filter, a transcoding filter design unit, and an encoding unit of an output CELP codec. By substituting a post-filter and a perceptual weighting filter of a prior art with one transcoding filter, the calculation amount of the transcoder is reduced, and speech quality decoded at a receiving end is improved.

Transcoder for speech codecs of different CELP type and method therefor

We describe a voice transformation method which changes the source speaker's acoustic features to those of a target speaker. In the method acoustic features are divided into two parts, linear and nonlinear parts. Linear parts are characterized by LPC cepstrum coefficients which are obtained from LP analysis. The nonlinear part, which represents the excitation signal, is modelled by the long-delay nonlinear predictor using a neural net. Conversion rules for the excitation signal are generated by the average pitch ratio and the mapping codebook, and those for LPC cepstrum coefficients are based on the orthogonal vector space conversion. In addition, the spectral envelope compensation is proposed to correct spectral distortion. In the transformed speech a listening test shows that the proposed method makes it possible to convert speaker's individuality while maintaining high quality.

/pdf/a-new-voice-transformation-method-based-on-both-linear-and-10fjoqotve.pdf

A new voice transformation method based on both linear and nonlinear prediction analysis

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