A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.

Suppression of acoustic noise in speech using spectral subtraction

A secure telephone call management system is provided for authenticating users of a telephone system in an institutional facility. Authentication of the users of the telephone call management system is accomplished by using a personal identification number, biometric means, and/or radio frequency means. The secure telephone call management system includes accounting software capable of limiting access to the system based on funds in a user's account, and includes management software capable of implementing widespread or local changes to the system. The system monitors a conversation in the telephone call to detect a presence of a first characteristic in audio of the conversation, and terminates the telephone call if the first characteristic does not match a second characteristic of biometric information of a user or a called party.

Telecommunication call management and monitoring system with voiceprint verification

Speech and speaker recognition technology has made very significant progress in the past 50 years. The progress can be summarized by the following changes: (1) from template matching to corpus‐base statistical modeling, e.g., HMM and n‐grams, (2) from filter bank/spectral resonance to Cepstral features (Cepstrum + DCepstrum + DDCepstrum), (3) from heuristic time‐normalization to DTW/DP matching, (4) from gdistanceh‐based to likelihood‐based methods, (5) from maximum likelihood to discriminative approach, e.g., MCE/GPD and MMI, (6) from isolated word to continuous speech recognition, (7) from small vocabulary to large vocabulary recognition, (8) from context‐independent units to context‐dependent units for recognition, (9) from clean speech to noisy/telephone speech recognition, (10) from single speaker to speaker‐independent/adaptive recognition, (11) from monologue to dialogue/conversation recognition, (12) from read speech to spontaneous speech recognition, (13) from recognition to understanding, (14) from single‐modality (audio signal only) to multi‐modal (audio/visual) speech recognition, (15) from hardware recognizer to software recognizer, and (16) from no commercial application to many practical commercial applications. Most of these advances have taken place in both the fields of speech recognition and speaker recognition. The majority of technological changes have been directed toward the purpose of increasing robustness of recognition, including many other additional important techniques not noted above.

/pdf/fifty-years-of-progress-in-speech-and-speaker-recognition-2xcl6ijccl.pdf

Fifty years of progress in speech and speaker recognition

The paper continues the authors' investigation of machines that adaptively acquire language through interaction with a complex environment. In particular, the present work focuses on the problem of spoken word acquisition, using the authors' proposed principles to motivate a method to govern the emergence of word symbols from the speech signal. The mechanism involves a connectionist network embedded in a feedback control system. The resulting system has two unique characteristics. First, no text is utilized by the device, in contrast to all other speech understanding systems. Second, the vocabulary and grammar is unconstrained, being acquired by the device during the course of performing its task. This is also in contrast to all other systems, in which the salient vocabulary and grammar are preprogrammed. A rudimentary baseline experiment is described, involving 1105 natural language utterances in an automated call routing application scenario. >

An experiment in spoken language acquisition

Disclosed are systems and methods which provide availability of information on a network wide basis, with the network or information technology (IT) fabric spanning a wide range of institutions and other sources of information, including correctional facilities, without regard to which jurisdiction the source of information belongs, e.g. police, courts, federal investigation agencies, public databases etcetera. Embodiments provide an electronic based capability to identify useful information and for locating, collecting, compiling, aggregating, distilling, and/or reporting robust data.

Systems and methods for acquiring, accessing, and analyzing investigative information

The objective of this paper is to provide a summary of the state-of-the-art of automatic speech recognition (ASR) and its relevance to military applications. Until recently, speech recognition had its widest application in the development of vocoders for narrow-band speech communications. Presently, research in ASR has been accelerated for military tasks such as command and control, secure voice systems, surveillance of communication channels, and others. Research in voice control technology and digital narrow-band systems are of special interest. Much of the emphasis of today's military-supported research is to reduce to practice the current state of knowledge of ASR, as well as directing research in such a way as to have future military relevance. In coordination with the above-mentioned emphasis in military-supported research, this paper is divided into two major sections. The first section presents discussion of the state-of-the-art and problems in the various subareas of the ASR field. The second section presents a number of unsolved problems and techniques which need to be perfected before the solutions to a number of military applications of the ASR field are possible.

An assessment of the technology of automatic speech recognition for military applications

http://ieeexplore.ieee.org/iel6/29/26131/01163072.pdf

E. Neuberg

Papers

An assessment of the technology of automatic speech recognition for military applications

Correction to "An assessment of the technology of automatic speech recognition for military applications"