Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades

Singh, Nilu; Agrawal, Alka; Khan, Raees Ahmad

doi:10.18311/gjeis/2017/15973

Cited by 12 publications

(6 citation statements)

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“…Since then, various laboratories have attempted to develop speech recognition, but the accuracy has not exceeded 80%. The low accuracy of voice recognition is due to different accents, volume, degree of dialect, and background noise [35].…”

Section: Telemarketing and Technical Elements Of Alternative Systemsmentioning

confidence: 99%

Toward a Chatbot for Financial Sustainability

Hwang

Kim

2021

Sustainability

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This study examines technology effectiveness for industry demand in which artificial intelligence (AI) is applied in the financial sector. It summarizes prior studies on chatbot and customer service and investigates theories on acceptance attitudes for innovative technologies. By setting variables, the study examines bank revenue methodologically and assesses the impact of customer service and chatbot on bank revenues through customer age classification. The results indicate that new product-oriented funds or housing subscription savings are more suitable for purchase through customer service than through chatbot. However, services for existing products through chatbot positively affect banks’ net income. When classified by age, purchases by the majority age group in the channel positively affect bank profits. Finally, there is a tendency to process small banking transactions through the chatbot system, which saves transaction and management costs, positively affecting profits. Through empirical analysis, we first examine the effect of an AI-based chatbot system implemented to strengthen financial soundness and suggest policy alternatives. Second, we use banking data to increase the study’s real-life applicability and prove that problems in customer service can be solved through a chatbot system. Finally, we investigate how resistance to technology can be reduced and efficiently accommodated.

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Section: Telemarketing and Technical Elements Of Alternative Systemsmentioning

confidence: 99%

Toward a Chatbot for Financial Sustainability

Hwang

Kim

2021

Sustainability

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“…The experiment of this work is performed using clean speech corpus ARABIC_DB ("Al-Diri & Sharieh, 2000") 1 . The ARABIC_DB is considered as a speech data set specifically developed to help in characterizing speakers as individuals.…”

Section: Datasetmentioning

confidence: 99%

“…The collection of all code-words is called a codebook. K-means is a popular technique to design codebooks in VQ as illustrated in the algorithm (1). The data are thus significantly compressed, yet still accurately represented.…”

Section: Vector Quantization (Vq)mentioning

confidence: 99%

“…There are 3 types of recognition in speech signals: speech recognition, speaker recognition, and language identification. Speaker recognition can be defined as the verification and identification of a speaker [1]. Speaker identification is considered as the procedure that recognizes an unidentified speaker through the process of comparing his/her voice with voices of registered speakers in the database, it can be considered as one to-many comparisons (1: N match in which the voice will be put in comparison against N templates) [2].…”

Section: Introductionmentioning

confidence: 99%

“…Speaker identification can be divided into text-independent and text-dependent. The Text-independent system does not necessitate offering the same text/utterance for testing and training [1]. In the presented study, the MFCC feature is utilized to design text-independent SIS, in which it mimics human ear"s behavior and performs adequately in SIS.…”

Section: Introductionmentioning

confidence: 99%

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Arabic Speaker Identification System Using Multi Features

Mohammed¹,

Hassan²,

Ali³

2020

ETJ

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The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase. The second phase is testing, which searches the identification of the speaker. In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency. Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process. The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8%.

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