Design of Intelligent Customer Service Report System Based on Automatic Speech Recognition and Text Classification

In combination with features such as intensive labor and speech in the customer service report field, this paper discusses the design of a customer service report system based on artificial intelligence automatic speech recognition technology and big data text classification technology. The proposed system realizes functions like a flat IVR menu, quick transcription and input of work orders, dynamic tracking of failure hotspots, automatic classification and accumulation of the knowledge base, speech emotion detection and real-time supervision of service quality, and it can improve the user experience and reduce the labor strengths of customer service staff. The automatically accumulated knowledge base can further assist with feedback to resolve the difficult problem that the emerging intelligent network Q&A and intelligent robots rely on a manually summarized knowledge base.

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Automatic speech signal segmentation based on the innovation adaptive filter

International Journal of Applied Mathematics and Computer Science ◽

10.2478/amcs-2014-0019 ◽

2014 ◽

Vol 24 (2) ◽

pp. 259-270 ◽

Cited By ~ 9

Author(s):

Ryszard Makowski ◽

Robert Hossa

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Adaptive Filter ◽

Speech Signal ◽

Detection Efficiency ◽

Difficult Problem ◽

Second Order ◽

Signal Segmentation ◽

Schur Algorithm ◽

Starting Point

Abstract Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006), and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

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Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

10.21203/rs.3.rs-41431/v1 ◽

2020 ◽

Author(s):

Rianto Rianto ◽

Achmad Benny Mutiara ◽

Eri Prasetyo Wibowo ◽

Paulus Insap Santosa

Keyword(s):

Customer Service ◽

Text Classification ◽

Formal Language ◽

Text Processing ◽

Support Vector ◽

Symbol System ◽

Verbal Language ◽

Linguistic Rules ◽

Service Staff ◽

Airline Ticket

Abstract As social beings, humans always interact with one another using either verbal or non-verbal language. Language is an arbitrary sound-symbol system, which is used by members of a community to cooperate, interact, and identify themselves. Indonesian language is classified into two categories, namely formal and non-formal. The former meets the grammatical standard as prescribed by linguistic rules of the language, while the latter tends to deviate it. In daily communication, however, non-formal language is more intensively used because they are more practical and easier to understand. With this tendency, non-formal language causes problems in linguistic computation because most linguistic computations use formal standard languages that already have standardized rules. This research aims to develop a dynamic Indonesian closed corpus related to airline ticket reservation, namely "Incorbiz". The "Incorbiz" will be used as stemming tool for formal and non-formal Indonesian. Text processing, text normalization, and auto-update data were proposed in this research. This research also compared two stemming techniques i.e. "Sastrawi" and "Incorbiz" to process the 30-sample dataset. The algorithm used to process the classification is Support Vector Machine (SVM). The data used to develop the "Incorbiz" were taken from conversations between customer service staff and consumers in airline ticket reservations. The result showed that "Incorbiz" had higher accuracy than "Sastrawi" on 0.89 and 0.67, respectively.

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