The DKU-LENOVO Systems for the INTERSPEECH 2019 Computational Paralinguistic Challenge

Wu, Haiwei; Wang, Weiqing; Li, Ming

doi:10.21437/interspeech.2019-1386

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…In [22], a .367 of CC was obtained by an early fusion of the learnt representations from attention and sequence to sequence autoencoders. Fisher Vector encodings were fused with the outputs of the ComParE Functionals in [23] to get a .365 of CC. In both [24] and [25], CNNs were exploited in an end-to-end deep learning approach: no fusion techniques are executed in the former study to get a .335 of CC; in the latter, a fusion of their CNN models was made to get a .325 of CC score.…”

Section: Resultsmentioning

confidence: 99%

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Egas-López

Gosztolya

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Estimating the degree of sleepiness from the human speech is an emerging research problem with straightforward applications. In this study, we employ the x-vector approach, currently the state-of-the-art in speaker recognition, as a neural network feature extractor to detect the level of sleepiness of a speaker. Besides using different corpora for fitting the xvector DNN, we also experiment with adding noise and reverberation to the training samples. According to our experimental results for the publicly available Dusseldorf Sleepy Language Corpus, utilizing x-vector embeddings as features for Support Vector Regression consistently leads to competitive performance scores in sleepiness detection. In particular, we present the highest Spearman's correlation coefficient on the public corpus that was achieved by a single method.

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Section: Resultsmentioning

confidence: 99%

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Egas-López

Gosztolya

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Feature aggregation has been a key component of successful systems for speech paralinguistic tasks [64]- [66], and in recent years we have also had success using these methods [67]- [69]. In this work, we experimented with three types of feature aggregation methods that are based on functionals, Bag of Words [70], and Fisher Vector Encoding [71].…”

Section: Feature Aggregationmentioning

confidence: 99%

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Syed

Lech

et al. 2021

IEEE Access

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Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neuralembeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer's Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.

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“…There are also benchmarking solutions to the LID. Google compact language detector (CLD) and TextCat employ n-gram based method 1 , LogR [20] uses a discriminative strategy with regularized logistic regression [16]. Cavnar and Trenkle [14] provided outstanding results compared to the other state-of-the-art methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Natural language processing (NLP) techniques play an important role in the classification and processing of huge digital documents on the Web [1,2]. Determination of the language of a text's content is called Language Identification (LID classification).…”

Section: Introductionmentioning

confidence: 99%

A Signal Processing Method for Text Language Identification

Hassanpour

AlyanNezhadi

Mohammadi

2021

IJE

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Language identification is a critical step prior to any natural language processing. In this paper, a signal processing method for Language Identification is proposed. Sequence of characters in a word and the order of words in stream identify the language. The sequence of characters in a stream provides a signature to recognize the language without understanding its meaning. The signature can be extracted using signal processing techniques via converting texts into time series. Although several research and commercial software have been developed to identify text language, they need a standard dictionary for each language. We proposed a dictionary independent method consisting of three main steps, I) preprocessing, II) clustering and finally III) classification .First, the texts are converted to time series using UTF-8 codes. Second, to group similar languages, the obtained series are clustered. Third, each cluster is decomposed into 32 sub-bands using a Wavelet packet, and 32 features are extracted from each subband. Also, a multilayer perceptron neural network is used to classify the extracted features. The proposed method was tested on our dataset with 31000 texts from 31 different languages. The proposed method achieved 72.20% accuracy for language identification.

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The DKU-LENOVO Systems for the INTERSPEECH 2019 Computational Paralinguistic Challenge

Cited by 12 publications

References 24 publications

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

A Signal Processing Method for Text Language Identification

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