Robust Speech Recognition of Uncertain or Missing Data

Kolossa, Dorothea; Häb‐Umbach, Reinhold

doi:10.1007/978-3-642-21317-5

Cited by 34 publications

(3 citation statements)

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“…The speaker-dependent models for the ASR system were 51 single-stream left-to-right word HMMs and one additional HMM for silence (384 states for audio and 128 states for video). All experiments were carried out with our Java Audiovisual SPEech Recognizer JASPER [3].…”

Section: Methodsmentioning

confidence: 99%

“…It has been shown that with a combination of audio and video data it is possible to increase speech recognition accuracy when compared to ASR systems that employ audio data streams only (e.g., [1,2]). Currently, the main techniques used for the integrated decoding of audio and video streams are coupled HMMs (CHMM, [3]), turbo-decoding (TD, [4]), and approaches based on deep learning [5,6,7]. In dealing with the integration it is important to control the contribution of each stream within the decoding procedure in order to ensure an optimal performance.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Stream Weighting for Turbo-Decoding-Based Audiovisual ASR

et al. 2016

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Automatic speech recognition (ASR) enables very intuitive human-machine interaction. However, signal degradations due to reverberation or noise reduce the accuracy of audio-based recognition. The introduction of a second signal stream that is not affected by degradations in the audio domain (e.g., a video stream) increases the robustness of ASR against degradations in the original domain. Here, depending on the signal quality of audio and video at each point in time, a dynamic weighting of both streams can optimize the recognition performance. In this work, we introduce a strategy for estimating optimal weights for the audio and video streams in turbo-decodingbased ASR using a discriminative cost function. The results show that turbo decoding with this maximally discriminative dynamic weighting of information yields higher recognition accuracy than turbo-decoding-based recognition with fixed stream weights or optimally dynamically weighted audiovisual decoding using coupled hidden Markov models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Stream Weighting for Turbo-Decoding-Based Audiovisual ASR

et al. 2016

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“…Traditionally, two of the main linear classification methods are Linear Discriminant analysis (LDA) and Logistic regression or Logit regression (Logit). Details on the LDA methodology can be found in Kolossa and Haeb-Umbach (2011), while for a description of the Logit regression structure see Boyacioglu (2009) and Tinoco and Wilson (2013). Other linear regression methods can be found in Varmuza and Filzmoser (2009).…”

Section: Other Classification Methodsmentioning

confidence: 99%

Bankruptcy Prediction Using Support Vector Machines and Feature Selection During the Recent Financial Crisis

Dellepiane¹,

Marcantonio²,

Laghi³

et al. 2015

IJEF

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This study aims at identifying an optimal set of features for predicting firms bankruptcy events in the current macroeconomic context. To this aim, among many financial features, we propose new country-specific factors which consider the macroeconomic conditions of the countries where firms operate. Our forecasting model is based on Support Vector Machines (SVMs), which are tools employed in supervised learning. Firstly, starting from a wide set of variables commonly used for bankruptcy prediction we assess the general effectiveness of SVMs also in comparison with the performances of other commonly used methods. Secondly, we try to improve the accuracy of forecasts by selecting optimal subsets of variables through a feature selection method. The results show that, in the current socio-economic context, the conjunct use of SVMs and the proposed feature selection technique significantly improves the accuracy of bankruptcy predictions compared to the performance of the other methods examined. Furthermore, we show that the proposed country-specific factors are relevant information for predicting the failure of firms and that most of the ratios proposed by Altman in 1968 are still relevant nowadays.

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