Building Domain Specific Languages for Voice Recognition Applications

This paper presents a method of implementing the voice recognition for the control of software applications. The solutions proposed are based on transforming a subset of the natural language in commands recognized by the application using a formal language defined by the means of a context free grammar. At the end of the paper is presented the modality of integration of voice recognition and of voice synthesis for the Romanian language in Windows applications.voice recognition, formal languages, context free grammars, text to speech

Voice Recognition and Mobility in the Legal Industry

In a typical legal work environment, attorneys work with their staff to generate and send case related legal documents and communications. Traditionally the attorney will dictate to a device capable of recording audio and the legal assistant will transcribe the audio directly from the source. In the early days of recorded dictation audio was recorded and saved to analog tape. Once the technology became available, dictation was saved digitally to flash memory and transmitted to hard disk for playback by the legal assistant. It has been this way for years, and due to advances in voice recognition technology and computer processing there are alternative options to the traditional dictation/transcription process. The focus of this paper is to examine the traditional process of dictation/transcription and how it compares to the process of using voice recognition software. Analysis of each process as well as an evaluation of voice recognition software will be performed. The document generation process will also be examined as it relates to transcription and creating a document, regardless of the content. The most efficient solution which benefits a small to medium size law firm will be recommended. According to Understanding How Law Offices Do Business, a small law firm has between one and ten lawyers and a mid-size law firm has up to 50 lawyers. These firms are the target audience. The goal of this paper is to determine if the use of voice recognition software can help an attorney and their staff be more efficient, and if so, which voice recognition software and methods work the best. Tests will be performed analyzing both Dragon Naturally Speaking 12 Professional and Windows 7 voice recognition software on the desktop. The software with the higher accuracy rate based on our tests will be used to evaluate voice recognition processes throughout this paper

Machine learning for voice recognition

Verbal communication is very important to us humans, but using thisperforming verbal communication to communicateion with machines still faces particular challenges. Therefore, researchers are trying to find ways to make communication with a machine more similar to communicating with other people, for which two systems have been identified: speech and voice recognition. While speech recognition has aimed to become speaker independent, voice recognition focuses on identifying the speaker, by looking at the tone of the voice, which is affected by the physical characteristics of that person. This requires one to identify these unique tonal features, to then train a system with this data. Being able to perform this identification well, would also bring benefit to speech recognition by allowing the system to adjust to the characteristics of that speaker and how he/she produces their sounds

Assessment of Classifiers for Potential Voice-Enabled Transportation Apps

Transportation apps are playing a positive role for today’s technology-driven users. They provide users with a convenient and flexible tool to access transportation data and services, as well as collect and manage data. In many of these apps, such as Google Maps, their operations rely on the effectiveness of the voice recognition system. For the existing and new apps to be truly effective, the built-in voice recognition system needs to be robust (i.e., being able to recognize words spoken in different pitch and tone). The goal of this study is to assess three post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy) to enhance the commonly used Google’s voice recognition system. The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that post-processing techniques could significantly enhance Google’s voice recognition system

Enduring voice recognition in bonobos

We would like to thank the French Ministère de l’Enseignement Supérieur et de la Recherche (PhD grant to SK), the Université de Saint-Etienne (research sabbaticals to FL and NM, visiting professorship to KZ and research funding) and the European Research Council (KZ grant PRILANG 283871).Long-term social recognition is vital for species with complex social networks, where familiar individuals can encounter one another after long periods of separation. For non-human primates who live in dense forest environments, visual access to one another is often limited, and recognition of social partners over distances largely depends on vocal communication. Vocal recognition after years of separation has never been reported in any great ape species, despite their complex societies and advanced social intelligence. Here we show that bonobos, Pan paniscus, demonstrate reliable vocal recognition of social partners, even if they have been separated for five years. We experimentally tested bonobos' responses to the calls of previous group members that had been transferred between captive groups. Despite long separations, subjects responded more intensely to familiar voices than to calls from unknown individuals - the first experimental evidence that bonobos can identify individuals utilising vocalisations even years after their last encounter. Our study also suggests that bonobos may cease to discriminate between familiar and unfamiliar individuals after a period of eight years, indicating that voice representations or interest could be limited in time in this species.Publisher PDFPeer reviewe

Voice recognition in safety systems

This article discusses the problem of biometric way of identifying a person, in particular, voice recognition for safety system. The algorithm for obtaining the speaker's voice model is described. In this paper, the method of Mel-frequency cepstral coefficients (MFCC) is considered for distinguishing the distinctive features of the speaker. Besides, speaker recognition by voice may be used in criminal investigations, forensics and radio reconnaissance