Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Speech-to-text transcription <s> Research in automatic speech and speaker recognition has now spanned five decades. This paper surveys the major themes and advances made in the past fifty years of research so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. Although many techniques have been developed, many challenges have yet to be overcome before we can achieve the ultimate goal of creating machines that can communicate naturally with people. Such a machine needs to be able to deliver a satisfactory performance under a broad range of operating conditions. A much greater understanding of the human speech process is required before automatic speech and speaker recognition systems can approach human performance. <s> BIB001 </s> Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Speech-to-text transcription <s> In recent years, there has been dramatic progress in both speech and language processing, in many cases leveraging some of the same underlying methods. This progress and the growing technical ties motivate efforts to combine speech and language technologies in spoken document processing applications. This paper outlines some of the issues involved, as well as the opportunities, presenting an overview of the special double session on this topic. <s> BIB002
Speech-to-text (or automatic speech recognition) systems convert a speech signal into a sequence of words. Progress in the field has been driven by standardised metrics, corpora and benchmark testing through NIST (US National Institute of Standards and Technology) since the mid-1980s, with systems developed for increasingly challenging tasks or ‗speech domains': developing from the domain of single person dictation systems to today's research into systems for the meetings and lectures domain. A brief history of speech (and speaker) recognition research can be found in BIB001 . There can be substantial differences between speech domains, and this can create difficulty for automatic systems. For example, speech from the lecture domain has much in common with speech from a more conversational domain including extraneous filler words (like ‗okay') and filled pauses (‗uh'). Sentences often exhibit false starts or are incomplete. It also displays poor planning at higher structural levels, often digressing from the primary theme. Generally, speech recognition systems developed for one domain cannot be employed as a black box that can handle any domain: even speech from the same domain that differs from the ‗training' data may be problematic (e.g. speech from previously unseen news broadcasts in  . Despite significant progress in adaptation techniques to compensate for such differences, the development of systems which are robust to differences in data is a key research goal at present  BIB002 . Consequently, performance measurements are specific to a particular task and dataset, and not broadly representative. An evaluation in 2004 reported state-of-the-art transcription systems to achieve a ‗word error rate' of 12% for broadcast news in English, but 19% for Arabic. For conversational telephone speech, the figures were 15% for English and 44% for Arabic  . The effect of different recording techniques can be seen in  , where error rates for meetings were 26% when each participant had their own headset microphone, and 38% when the recordings were via multiple distant microphones. There is a computation time versus accuracy trade-off: a real-time system will typically perform less well than a 10-times-real-time or even unconstrained system, but the degradation will vary with situation. Similarly, memory constraints can affect performance. State-of-the-art systems typically use hardware beyond that of today's average desktop. (The word-error rates for English speech referred to above were achieved in 10 times real time for broadcast news and 20 times real time for conversational telephone speech  .)  shows that speech-to-text transcription researchers have achieved 15 years of continuous error rate reduction: we might wonder what the future holds. At present, the accuracy of current systems lags about an order of magnitude behind the accuracy of human transcribers on the same task  ; David Nahamoo quoted in Howard-Spink, n.d.). Moore has estimated that it would take a minimum of 600,000 hours of acoustic training data to approach a zero error rate using current techniques, which he notes is at least four times a typical human's lifetime exposure to speech! 