Acoustic Similarity Scores for Keyword Spotting

Veiga, Arlindo; Lopes, Carla; Sá, L. de; Perdigão, Fernando

doi:10.1007/978-3-319-09761-9_5

Cited by 6 publications

(2 citation statements)

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“…This loss function is actually a measure of the difference between the actual output and predicted output. We have used categorical crossentropy loss function as defined by Equation (5).…”

Section: Weights Biases and Hyperparametersmentioning

confidence: 99%

“…Apart from these devices and systems, speech processing is making a significant impact in healthcare, such as diagnosis of Parkinson's disease [1], Dementia [2], and Alzheimer's disease [3]. Other applications include wearable for deaf persons [4], keyword spotting [5], emotion recognition [6], accent classification [7], and language identification [8]. Accordingly, research employing speech processing is booming with a particular interest in speech recognition, classification, and generation.…”

Section: Introductionmentioning

confidence: 99%

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How many Mel‐frequency cepstral coefficients to be utilized in speech recognition? A study with the Bengali language

Hasan

Hossain

2021

The Journal of Engineering

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Speech‐related research has a wide range of applications. Most speech‐related researches employ Mel‐frequency cepstral coefficients (MFCCs) as acoustic features. However, finding the optimum number of MFCCs is an active research question. MFCC‐based speech classification was performed for both vowels and words in the Bengali language. As for the classification model, deep neural network (DNN) with Adam optimizer was used. The performances were measured with five different performance metrics, namely confusion matrix, classification accuracy, area under curve of receiver operating characteristic (AUC‐ROC), F1 score, and Cohen's Kappa with four‐fold cross‐validations at different number of MFCCs. All performance metrics gave the best score for 24/25 MFCCs; hence it is suggested that the optimum number of MFCCs should be 25, although many existing studies use only 13 MFCCs. Furthermore, it is verified that increasing the number of MFCCs yields better classification metrics with lower computational burden than the increment of hidden layers. Lastly, the optimum number of MFCCs obtained from this study was used in a more improved DNN model, from which 99% and 90% accuracies were achieved for vowel and word classification, respectively, and the vowel classification score outperformed state‐of‐the‐art results.

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“…This loss function is actually a measure of the difference between the actual output and predicted output. We have used categorical crossentropy loss function as defined by Equation (5).…”

Section: Weights Biases and Hyperparametersmentioning

confidence: 99%