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statistical models of language to disambiguate alternative interpretations of the audio track The process of speech perception selects from among the top N hypothetical phrase transcripts, in AACR using <Scene/> level expectations to assist in the resolution of uncertainties Speech recognition APIs enable one to embed ASR in AACR The CRA integrates ASR as a black box capability Phrases may be learned and associated with functions to enable AACR user dialogs, but that capability may not mature until the iCR Meanwhile, commercial speech recognition tools like IBM Embedded ViaVoice, Enterprise Edition can improve accuracy, such as the N-Best technology, which returns multiple phrase matches so the AACR can select the best phrase for the <Scene/> and <UDH/> IBM s JAVA supports Java Speech API (JSAPI) with multiple Java voice applications to share the same Java Virtual Machine (JVM) IBM s Mastor is a multilingual speech-to-speech translator variant of ViaVoice [247].

doubted that George Sisler would talk to me without Harvey. Anyone running a zillion-dollar hedge fund wasn t going to just waste his time on someone like me. Perhaps Harvey had told George that I had taken harebrained trades and lost my account, in which case a meeting with George would even be less likely. So I was surprised when George took my call the next day. I called him as soon as I arrived at Wakeman at 7:00 A.M., and he picked up the phone. George Sisler. I had anticipated a secretary or a receptionist. I paused to collect my thoughts, and in that small amount of time, George hung up the phone. Obviously, he was very busy. I called and this time he answered again: George Sisler. Who is this It s Harry Banes. The guy that Harry! Did you do your interest rate homework I did. I did a lot of work. But I have some questions before I can continue. I ll see you at lunch, he told me, and then he hung up. I worked my brains out that morning so that I could spare a couple of hours for lunch. It had been a personal goal of mine to not leave for lunch at all in the next few weeks, so that I could pay back time lost at the firm. But a meeting with George Sisler took precedence over that for now. At lunchtime, I hopped in a cab and hoped that I wasn t making a mistake by showing up without Harvey.

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Space-time routing [18]

The Microsoft Speech Application SDK (SASDK) is based on the Speech Application Language Tags (SALTs) speci cation for Web applications Microsoft SDK related products include Maxxam speech-enabled applications for banks and credit unions based on Microsoft Speech Server and Speech Application SDK [248] While the spoken utterance itself holds clues to the identity of the speaker, AACRs need to know speaker identity as accurately as possible Speaker identi cation (ID) has long been of research interest [250] Speaker models characterize the dominant tones and prosody features of a segment of audio If the audio segment consists primarily of speech of one speaker, the resulting model re ects the speech patterns of the speaker Sometimes, the acoustic patterns of the background corrupt or even dominate the acoustic patterns, so speaker ID in a moving automobile will in part identify the automobile through its acoustics.

Epidemic/ Random spray [20 24]

Speech perception shares concepts and methods with text processing, information extraction, and document retrieval Performance varies substantially along the critical dimensions of vocabulary size, quality of articulation (eg, isolated words versus continuous coarticulated speech), and user-speci c training (none versus speaker-dependent session) In addition, noise backgrounds and acoustic scene complexity (eg, newsroom versus cocktail party) also affect performance In challenging scenes word error rates on the order of 40% are common Commercial speech processing software may achieve a 50% word error rate for general untrained speech or 20 30% for fully trained speaker-dependent recognizers in low background noise [249] Statistical language models can correct word errors to a lower phrase error rate 1024 Perceiving Noise and Interference.

Network coding [40]

Many <Scenes/> have intense vehicle noise, for example, from automobiles, trains, earth-moving equipment, aircraft, and the like. Sports enthusiasts (NASCAR comes to mind), construction workers, longshoremen, and the like

It didn t seem to matter George saw me right away The office wasn t any different from the last time I d seen it Just as dumpy as before This time I had a new appreciation for it I started to wonder what George s house looked like Did he live in his car, or under a bridge I would soon find out But first he had questions for me Lots of questions What did you find he asked You specifically asked me to find out if I could figure out a way to take the carry trade on a retail platform, I answered That was my focus I stayed up all night on Friday and did a ton of research on it And You found that it s impossible This was a statement of fact, meant to encourage me to move on to the next point.

Tree approach [19]

Erasure coding [38, 39, 41]

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