Noise adaptation of HMM speech recognition systems using tied-mixtures in the spectral domain

Erell, A.; Burshtein, David

doi:10.1109/89.554271

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…The EM iteration is now described by (15), (16), and by the following equation that replaces (17): Tied variances enable a more compact representation, that is, when tying is applied, only variance parameters are required (instead of ), thus lowering memory requirements.…”

Section: A Tied Variancesmentioning

confidence: 99%

“…Now, recalling the Gaussian assumption for , and invoking the integration required by (11), we obtain (12) where (13) Our estimate, , is calculated using (9), (10), (12), and (13). In [16] we used in order to design a noise robust speech recognition system and compared it to alternative noise adaptation methods using the MIXMAX approach. For our present speech enhancement application the reconstructed speech signal, , for the current frame is given by Note that the reconstructed phase angle is the original phase angle of the noisy speech, as is usually the case when using spectral-domain enhancement methods [2].…”

Section: Application To Speech Enhancementmentioning

confidence: 99%

“…The density of given the th mixture, , is obtained by differentiating (7), [15] The probability density of is hence given by (8) Nádas et al used a probabilistic rule based on (8) to adapt a discrete density HMM-based speech recognition system in the presence of additive noise. In [16] the MIXMAX model is used in order to adapt other HMM-based speech recognition systems to noise, including systems that use continuous mixture of Gaussians and systems that utilize time derivative (delta) spectral features.…”

Section: Hencementioning

confidence: 99%

“…In [15], the MIXMAX model is used to design a noise adaptive, discrete density, HMM-based, speech recognition algorithm. In [16], we used the MIXMAX model to design various noise adaptive, continuous density, HMM-based speech recognition systems. In this paper, our approach is more similar to the adaptation algorithm presented in [16], when the feature vector comprises all the elements of the DFT of the frame (instead of the MEL spectrum used in [16]).…”

Section: Introductionmentioning

confidence: 99%

“…In [16], we used the MIXMAX model to design various noise adaptive, continuous density, HMM-based speech recognition systems. In this paper, our approach is more similar to the adaptation algorithm presented in [16], when the feature vector comprises all the elements of the DFT of the frame (instead of the MEL spectrum used in [16]). We also discuss the computational complexity of the new speech enhancement algorithm and show how it can be reduced, essentially with no performance penalties.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Speech enhancement using a mixture-maximum model

Burshtein

Gannot

2002

IEEE Trans. Speech Audio Process.

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Abstract-We present a spectral domain, speech enhancement algorithm. The new algorithm is based on a mixture model for the short time spectrum of the clean speech signal, and on a maximum assumption in the production of the noisy speech spectrum. In the past this model was used in the context of noise robust speech recognition. In this paper we show that this model is also effective for improving the quality of speech signals corrupted by additive noise. The computational requirements of the algorithm can be significantly reduced, essentially without paying performance penalties, by incorporating a dual codebook scheme with tied variances. Experiments, using recorded speech signals and actual noise sources, show that in spite of its low computational requirements, the algorithm shows improved performance compared to alternative speech enhancement algorithms.Index Terms-Gaussian mixture model, MIXMAX model, speech enhancement.

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Section: A Tied Variancesmentioning

confidence: 99%

Section: Application To Speech Enhancementmentioning

confidence: 99%

Section: Hencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speech enhancement using a mixture-maximum model

Burshtein

Gannot

2002

IEEE Trans. Speech Audio Process.

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IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

Gupta

Singh²,

Singh

2023

Preprint

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Audio bots like Alexa, Siri, Google assistant, require a clean voice to perform a task. These bots ignore a disturbance or mixed voice. We get the famous message “sorry I could not understand”. With the introduction of smart homes and smart cities, it is imperative for devices to understand the commands in a noisy environment and that too in a native language. Indian Voice Separation and Enhancement (IVSE) offer the solution. For separation and enhancement of voice, the model should filter the noise first. For eliminating zero and negative values, a Zero-Negative filter (ZNF) is created. To eliminate the rippling effect induced by the time domain or frequency domain filters, Enhance Voice Function (EVF) enhances the voice. The output is windowed into different frames of equal lengths. Then it is labelled as noise and clean signals. A gradient boosting algorithm-based approach is then applied to filter the noise. The 50,000 voiceprints from the filtered voice were used to construct a training and validation set. For predictive analysis, the dataset is divided into an 80:20 ratio. IVSE uses LightGBM to create distinctive voiceprints. LightGBM operates in the background on Tensor Flow, which improves its performance. The paper compares IVSE and different benchmark algorithms at the end.

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VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

Gupta

Singh²,

Sinha³

2020

Preprint

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Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

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Noise adaptation of HMM speech recognition systems using tied-mixtures in the spectral domain

Cited by 4 publications

References 8 publications

Speech enhancement using a mixture-maximum model

Speech enhancement using a mixture-maximum model

IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

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