Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent"[1] systems. Systems that use training are called "speaker dependent".
PATCHED Att Text To Speech Setup With Audrey Voice (make Your Pc Speak)
Speech recognition applications include voice user interfaces such as voice dialing (e.g. "call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search key words (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), determining speaker characteristics,[2] speech-to-text processing (e.g., word processors or emails), and aircraft (usually termed direct voice input).
Described above are the core elements of the most common, HMM-based approach to speech recognition. Modern speech recognition systems use various combinations of a number of standard techniques in order to improve results over the basic approach described above. A typical large-vocabulary system would need context dependency for the phonemes (so phonemes with different left and right context have different realizations as HMM states); it would use cepstral normalization to normalize for a different speaker and recording conditions; for further speaker normalization, it might use vocal tract length normalization (VTLN) for male-female normalization and maximum likelihood linear regression (MLLR) for more general speaker adaptation. The features would have so-called delta and delta-delta coefficients to capture speech dynamics and in addition, might use heteroscedastic linear discriminant analysis (HLDA); or might skip the delta and delta-delta coefficients and use splicing and an LDA-based projection followed perhaps by heteroscedastic linear discriminant analysis or a global semi-tied co variance transform (also known as maximum likelihood linear transform, or MLLT). Many systems use so-called discriminative training techniques that dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of the training data. Examples are maximum mutual information (MMI), minimum classification error (MCE), and minimum phone error (MPE).
A well-known application has been automatic speech recognition, to cope with different speaking speeds. In general, it is a method that allows a computer to find an optimal match between two given sequences (e.g., time series) with certain restrictions. That is, the sequences are "warped" non-linearly to match each other. This sequence alignment method is often used in the context of hidden Markov models.
Speech recognition is also very useful for people who have difficulty using their hands, ranging from mild repetitive stress injuries to involve disabilities that preclude using conventional computer input devices. In fact, people who used the keyboard a lot and developed RSI became an urgent early market for speech recognition.[106][107] Speech recognition is used in deaf telephony, such as voicemail to text, relay services, and captioned telephone. Individuals with learning disabilities who have problems with thought-to-paper communication (essentially they think of an idea but it is processed incorrectly causing it to end up differently on paper) can possibly benefit from the software but the technology is not bug proof.[108] Also the whole idea of speak to text can be hard for intellectually disabled person's due to the fact that it is rare that anyone tries to learn the technology to teach the person with the disability.[109]
The alternative approach to speech synthesis is to create allsound from scratch without any samples of real human voices. Thesefully synthetic speech synths are preferred by most serious speechusers. As the voice is fully synthetic, it's also customizablemeaning you can do your own voices adjusting parameters like headsize, breathiness and roughness. Fully synthetic speech isgenerally more responsive (less delay before it speaks) and scalesbetter to be used at high rates. It is also easier to domultilingual synths if you generate all the sound on the fly. Thetech term for this type of synthesis is formant based or rulebased. mixing samples and formants is also possible though rare asis physically modelling the organs that actually produce thespeech, as the latter tends to be computationally intensive.
I used the Supernova screen reader in the document read mode toread the text. As some of the older releases said end of document"at the end without inserting a pause first, I had to manually cutoff that part at the end of the audio file. Because some speechsynths considered Supernova's prompting to be part of the lastsentence of the text, you might have a feeling that the voice iscut mid-sentence in some of the old audio files here. There was notmuch I could do about it.
Where as Microsoft's SAPI 4 intonation varied wildly and that ofMary in SAPI 5 did not, Microsoft Anna is of the more flutteryvariety. The intonation reminds me of TV channels like Euron News.The voice does sound particularly pleasant in rather formal contextsuch as announcements, neutral Windows user interface text, asspoken by a screen reader, and in passages like still animprovement. This intonation is one of the reasons why I like thespeech in Vista so much. But then again, at times the intonationgoes over the top. Sometimes the voice sounds angry or just wildlyrandom, at other times it manages to be very bland. Listen to thesnippet one word, though do keep in mind that the exclamation markaffects matters, too. I don't seem to be able to detect muchdifference in intonation between questions and exclamations,either, which is a definite minus. The two use an intonationmarkedly different from normal sentences, however, the intonationdoes not convey to me, as a second-language English speaker, anysense of questioning or shouting, so I'd say it's rratherunintuitive after all.
IBM ViaVoice is IBM's version of the popular Eti Eloquencesynth. I have heard samples of both and they seem to containexactly the same voice engine. Another speech synthesizer havingthe engine for Linux is Voxin. The synth is free in the sence thatonce you purchase a professional screen reader, it is most likelyincluded with it. Jaws and Windoweyes come with Eloquence whileSupernova users can purchase ViaVoice in a fraction of theoriginal, very high retail price. You can also get ViaVoice forfree by downloding a demo of IBM's speaking Web browser calledHomepage Reader.
Not all voices are born equal, however. Especially those with asmall memory footprint suffer from various artifacts. On the otherhand, it seems many voices have improved loads in this regard inversion 4, Amy used to be somewhat unclear in V3 but is not anymore. As a quick diversion regarding the lower quality voices let'sspend a while with Diane. It has a definite bass boost in it, manyEQed radio voices come to mind. I also find this voice not to be asintelligible as the two I've sampled and it is studdery. come tomind, for example, is said come PAUSE to mind with a truely strangeG-man:ish intonation. There are various pops and clicks whichmanifest themselves easily if you interrupt the speech while it isspeaking. Maybe snapping Dian to a zero crossing might help. TheEQed sound is, on the whole, most likely intentional and I feelthis voice would suit commercials well.
One definite merit of this synth is that the intonation is notonly language but also voice specific. The UK English voiceLawrence is a wonderful example of this. It speaks slower thanCallie, with a distinctly British singsong intonation. The voicebrings some BBC radio plays to mind and is a classic case of anelderly absent-minded professor. The style isn't for everyone orfor all kinds of text. It works extremely well in some computercontexts (e.g. Smart Humor in Programming Perl) and fairytails, forexample. Lawrence is definitely one of my all-time favorites as faras intonation is concerned. Still like the AT&T voices,achieving this great intonation does involve the risk of guessingwrong. Placing heavy emphasis on words like of course or thevarying rate in sentences such as of to the editor, is sometimesout of place. In addition, do notice the vibrato in the word what.Yet again, I can see marked improvements from the 3.x release ingeneral, though.
There are some pitch shifter, flanger and other delay effectpresets which can be applied to a voice, The feature is ratherboring once the novelty wears off. You can associate a set ofparameter deviations with a voice which is called a voice alias.The aliases aren't accessible within SAPI 5, though. this means theFX voices are out for SAPI users meaning most speaking apps outthere. MS did it much better in their SAPI 4 voices, in which FX istied to individual SAPI 4 voices. It would help if you could tweakthe effect parameters yourself. I've also discovered some issuesregarding the aliases. A straight alias of Callie which is slightlyslower and has the old robot effect doesn't reapply the effect whenyou pick it in the included Swift Talker applet (V4.1.4). Furthermore, I think it's pretty bad that some of the FX actually resultin digital clipping distortion such as patching old robot toLawrence, on this machine at least. It might be a volume controlissue if I'm unlucky, however.
The last point I'd like to address is responsiveness which Iranked too favorably the last time. The latency associated withvoices is bad enough to render them hard-to-use with a screenreader. Due to a small memory footprint switching between voicesdoesn't take all that much time. Neither does reading documents orpatiently waiting for the synth to complete before more speaking isqueued to it. The trouble is, this is rarely the case with dailyspeech users. Screen reader users are lazy and speech is stillrather slow and linear. Thus, people tend to skip to the next itemin the tab order or list as soon as they know the current one isnot desired. This is done by interrupting the speech by pressingtab for instance. So the lag from the interruption to the start ofthe next phrase should be as small as possible. The synth appearsto fail rather miserably in such scenarios. The delay issubjectively at least half a second with a gig of RAM and a 1.8 GHzPentium M. And it's not only navigating dialogs and windows butextends to typing in words rapidly or cursoring around a document.I'm taking 1.5 points from the high rates score due to this. 2ff7e9595c
Comments