MAS.632 Conversational Computer Systems - MIT OpenCourseWare

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Pftrn Med Speh RP itin word to build a more robust template. Once templates are trained successfully, the user can save them as a disk file on the host computer and reload them before each session, to avoid template training each time the recognizer is used. This recognizer also allows retraining of a single template, in which case the new LPC frames replace the old. Retraining is useful if the vocabulary changes, if the original training was poor, or if the user decides that a different pronunciation of the word is more comfortable. Retraining may also be necessary when the user has a cold, due to an obstructed nasal cavity. Word recognition requires the detection and classification ofspeech input. Just as with template creation, a word is captured by detecting that the audio input exceeds the background noise threshold and then converting the input to LPC frames until the input again drops to the background level. The input word is then compared with each template; each frame or set of LPC parameters of the input is compared to the corresponding frame of the template. The frame-byframe error is the sum of the differences between each of the dozen or so LPC parameters; the error for the word is the sum of the errors for each frame. The template with the smallest word error most closely matches the audio input. If the error exceeds a rejection threshold, a failed recognition attempt is reported. Otherwise the recognizer reports the word corresponding to the closest template. Both the templates and the captured audio input are multidimensional vectors, with one degree-of-freedom per LPC parameter and one for time. But to illustrate pattern matching let us assume that the templates consist of only a pair of values that display on two axes. One parameter is assigned to the X-axis and the other parameter maps to the Y-axis so that each template occupies a point whose coordinates are the two parameters (see Figure 7.3). Speech input is converted into this representation, i.e., a pair of parameters specifying a point in the same parameter space as the templates. The closest template is the nearest neighbor template to the point representing the input. The four templates divide the space into four regions; any pair of parameters in each region most closely matches the template in that region. Even the simplest of speech recognizers can improve its accuracy by employing a better pattern matching algorithm. Two additional refinements, illustrated in Figure 7.4, contribute to this recognition improvement. The first refinement is that the input cannot exceed a threshold distance (r in the figure) from the nearest template. When the audio input is further from the nearest template than this distance, a rejection error is reported. This is necessary to avoid inadvertently accepting noise, breath sounds, or a mistaken utterance as one of the acceptable words in the recognizer's vocabulary. The second refinement is a requirement that the input word maps significantly closer to the best match template than any other. If a point is very nearly the same distance from two or more templates, the recognizer is confident that one of these was spoken but uncertain which particular word was spoken. This is depicted in Figure 7.4 as the shaded regions around the lines partitioning the 135
136 VOICE COMMUNICATION WITH COMPUTERS Figure 7.3. To illustrate pattern matching decisions, assume that each template represents two degrees of freedom defining a Cartesian coordinate space Four templates occupy four points in this space. Figure 7.4. A more refined classifier has upper bounds r on the distance between an input specimen and a chosen template. It will also reject values which are closer to one template but also close to another, i.e., within distance d.
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VOICE COMMUNICATION WITH COMPUTEgS
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Speaking of Talk As we enter the ne
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Speaking ofTalk The short-term fix
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Preface This book began as graduate
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RFe[e ih This is just the beginning
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3 Speech Coding This chapter introd
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5 Speech Synthesis The previous two
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10 Basics of Telephones This and th
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11 Telephones and Computers The pre
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12 Desktop Audio Because of limitat
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Bibliography Ades, S. and D. C. Swi
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Biliograply o30 Daly, N. A. and V W
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Bib~lgraphy 309 Klatt, D. H. "Revie
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liblgraphy 311 G.Peterson, W. Wang,
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Bbliography 313 Spiegel, M. F "Usin
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Index p-law, 45, 220, 224 acoustics
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Integrated Services Digital Network
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vocal tract, 19-24, 23 voice mail,
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