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The Fundamentals of Text-to-Speech Synthesis
(Continued from Part 1)
From Diphone-based Synthesis to Unit Selection Synthesis
For the past decade, Concatenative Synthesis has been the preferred method in industry for creating high-intelligibility synthetic speech from text. Concatenative Synthesis is characterized by storing, selecting, and smoothly concatenating prerecorded segments of speech after possibly modifying prosodic attributes like phone durations or fundamental frequency. Until recently, the majority of concatenative TTS systems have been diphone-based. A diphone unit encompasses the portion of speech from one quasi-stationary speech sound to the next: for example, from approximately the middle of the /ih/ to approximately the middle of the /n/ in the word "in". For American English, a diphone-based concatenative synthesizer has, at a minimum, about 1000 diphone units in its inventory. Diphone units are usually obtained from recordings of a specific speaker reading either "diphone-rich" sentences or "nonsense" words. In both cases the speaker is asked to articulate clearly and use a rather monotone voice. Diphone-based concatenative synthesis has the advantage of a moderate memory footprint, since one diphone unit is used for all possible contexts. However, since speech databases recorded for the purpose of providing diphones for synthesis do not sound "lively" and "natural" from the outset, the resulting synthetic speech tends to sound monotonous and unnatural.
Any kind of concatenative synthesizer relies on high-quality recorded speech databases. An example fragment from such a database is shown in Figure 3. The top panel shows the time waveform of the recorded speech signal, the middle panel shows the spectrogram ("voice print"), and the bottom panel shows the annotations that are needed to make the recorded speech useful for concatenative synthesis.
In the top panel of Figure 3, we see the waveform for the words "pink silk dress". For the last word, dress, we have bracketed the phone /s/ and the diphone /eh-s/ that encompasses the latter half of the /eh/ and the first half of the /s/ of the word "dress". For years, expert labelers were employed to examine waveform and spectrogram, as well as their sophisticated listening skills, to produce annotations ("labels") such as those shown in the bottom panel of the figure. Here we have word labels (time markings for the end of words), tone labels (symbolic representations of the "melody" of the utterance, here in the ToBI standard [3]), syllable and stress labels, phone labels (see above), and break indices (that distinguish between breaks between words, sub-phrases, and sentences, for example).
Figure 3: Short Segment of a Speech Database for Concatenative TTS
It turns out that expert labelers need about 100-250 seconds of work time to label one second of speech with the set depicted in Fig. 3 [4]. For a diphone-based synthesizer, this might be a reasonable investment, given that a "diphone-rich" database (a database that covers all possible diphones in a minimum amount of sentences) might be as short as 30 minutes. Clearly, manual labeling would be impractical for much larger databases (dozens of hours). For this, we would require fully automatic labeling, using Speech Recognition tools. Fortunately, these tools have become so good, that speech synthesized from an automatically labeled speech database is of higher quality than speech synthesized from the same database that has been labeled manually [5].
Automatic labeling tools fall into two categories: automatic phonetic labeling tools to create the necessary phone labels and automatic prosodic labeling tools to create the necessary tone and stress labels, as well as break indices. Automatic phonetic labeling is adequate, provided it is done with a speech recognizer in "forced alignment mode" (i.e., with the help of the known text message so that the recognizer is only allowed to chose the proper phone boundaries but not the phone identities). The speech recognizer also needs be speaker-dependent (i.e., be trained on the given voice), and has to be properly bootstrapped from a small manually labeled corpus. Automatic prosodic labeling tools work from a set of linguistically motivated acoustic features (e.g., normalized durations, maximum/average pitch ratios) plus some binary features looked up in the lexicon (e.g., word-final vs. word-initial stress) [6], given the output from the phonetic labeling.
With the availability of good automatic speech labeling tools, Unit-Selection Synthesis has become viable for obtaining customer-quality TTS. Based on earlier work done at ATR in Japan [7], this new method employs speech databases recorded using a "natural" (lively) speaking style. The database may be focused on narrow-domain applications (such as "travel reservations" or "telephone number synthesis"), or it may be used for general applications like email or news reading. In the latter case, unit-selection synthesis can require on the order of ten hours of recording of spoken general material to achieve customer quality, and several dozen hours for "natural quality" (see footnote 2). In contrast with earlier concatenative synthesizers, unit-selection synthesis automatically picks the optimal synthesis units (on the fly) from an inventory that can contain thousands of examples of a specific diphone, and concatenates them to produce the synthetic speech. This process is outlined in Figure 4, which shows how the method must dynamically find the best path through the unit-selection network corresponding to the sounds for the word 'two'. The optimal choice of units depends on factors such as spectral similarity at unit boundaries (components of the "join cost" between two units) and on matching prosodic targets set by the front-end (components of the "target cost" of each unit). In addition, there is the problem of having anywhere from just a few examples in each unit category to several hundreds of thousands of examples to chose from. Obviously, also, the unit selection algorithm must run in a fraction of real time on a standard processor.
Figure 4: Illustration of Unit Selection for the Word "Two"
[Each node (circle) has a "target cost" assigned to it while each arrow carries a "join cost". The sum of all target and join costs is minimal for the optimal path (illustrated with bold arrows)]
In respect to quality, there are two good explanations why the method of unit-selection synthesis is capable of producing customer quality or even natural quality speech synthesis. First, on-line selection of speech segments allows for longer fragments of speech (whole words, potentially even whole sentences) to be used in the synthesis if they are found with desired properties in the inventory. This is the reason why unit-selection appears to be well suited for limited-domain applications such as synthesizing telephone numbers to be embedded within a fixed carrier sentence. Even for open-domain applications, such as email reading, however, advanced unit selection can reduce the number of unit-to-unit transitions per sentence synthesized and, consequently, increase the segmental quality of the synthetic output. Second, the use of multiple instantiations of a unit in the inventory, taken from different linguistic and prosodic contexts, reduces the need for prosody modifications that degrade naturalness [8].
Another important issue related to quality of the synthetic speech is the choice of the voice talent one records. A good voice can boost the quality rating of the synthesizer by as much as 0.3 Mean Opinion Scores (MOS) above that of an average voice (see footnote 3).
Speeding Up Unit Selection
As outlined so far, Unit-Selection Synthesis will run too slowly on any given computer. In practice, however, we need to be able to run many channels of TTS on a single processor. Note that in unit selection alone, 15% of the overall time is spent on candidate preselection and target cost computation, 78% is spent on join costs computation, and the rest (7%) is spent on the Viterbi search and on bookkeeping, etc. Several options exist to speed up unit selection. Clearly, one obvious way to reduce the computational effort it to calculate target costs only for the "top N" choices of a simpler/faster preselection search (equivalent to limiting the beam width of the main search). The effort for computing the full target costs even for a smaller set of candidates, however, may still be too large, because every potential choice must be evaluated, given the sequence of phones we want to synthesize in their context. One way to reduce the computational requirements is to precompute the target costs not for the exact set of units for a given context (only known at runtime), but precompute the target costs for a closely related ("representative") set of n-phone (e.g., n=3) contexts. This set turns out to be significantly smaller than the set of all units of a particular unit type, and, as such, leads to a speed-up by a factor of two, with no loss of synthesis quality.
More speed can be gained by attacking the join cost computation. Note that for an NxN transition, N2 join costs need to be computed. Since the join costs depend only on the database, but not on the current utterance, we can precompute them off-line. For this, we ran large amounts of text through the TTS system and collected information on which units get actually used. For example, we found that with 10,000 full news articles synthesized (the equivalent of 20 full days of reading), we encountered about 50 million joins. We actually used 85% of all units in the database, but only 1.2 million joins out of 1.8 billion available, that is, only 0.07%. "Caching" these join costs speeded up unit selection by a factor of four. Coverage, that is, synthesizing other text and evaluating how many of the chosen joins were in the cache, revealed a "cache hit rate" of about 99%. Note also that the fact that only a tiny subset of all possible joins between units in the large speech database gets used can be applied to reduce further the representative set of contexts for target cost computation (see above), resulting in a further reduction of the amount of computation needed. With these methods we succeeded in reducing the computational requirements enough to be able to run several dozens of channels on a standard PC processor.
Applications
One of the most compelling application of TTS today is in unified messaging. Figure 5 shows the Web interface of an experimental unified messaging system at AT&T Labs. Unified Messaging enables storing voice mail, email, and fax within one unified mailbox that is accessible via standard email clients, over the web, and, over the phone. When accessed over the standard telephone, email and fax messages need to be rendered in voice by using a TTS.
Figure 5: Web Interface for an Experimental Unified Messaging Platform at AT&T Labs
Another application is (selective) reading of Web pages. Here, like in email reading, special text "filters" extract the information requested over the web and deliver the related text to the TTS system that, in turn, renders the message by voice. Services exist that call up customers and alert them of stock prices having reached certain thresholds, etc. Similarly, emergency weather alerts are generated in form of text messages by the National Weather Service and are being read via TTS over TV channels and via the phone. Finally, with the increasing trend to keep one's virtual office data "on the web", access to critical elements such as calendar, appointment and address books, as well as other "know-how" may be provided to "road warriors" by TTS over the phone.
In the future, voice-enabled services such as the "How May I Help You?" (HMIHY) system from AT&T Labs [9] will revolutionize customer care and call routing. In customer care, customers call in with questions about a specific problem. In call routing, customers call a central number, but need to be connected to another location. TheHMIHY system, currently in limited deployment in AT&T, fully automatically handles billing inquiries, and 18 other frequent topics. Another variant of the system reduces the cost of providing operator services. HMIHY combines speech recognition, spoken language understanding, dialogue management, and text-to-speech synthesis to create an absolutely natural voice interface between customers and voice-enabled service. The user may chose any words, change his or her mind, hesitate, in short, speak completely "naturally". In all cases, the system reacts with a friendly voice.
Finally, in a world that is getting more "broadband" and more "multimedia" every day, TTS may provide a compelling interface by not only synthesizing audio, but also video. "Talking Heads" may serve as focus points in (multimedia) customer care, in e-commerce, and in "edutainment". As it did in audio-only TTS, also in Visual TTS, Unit-Selection synthesis provides the means to create compellingly natural-looking synthetic renderings of talking people [10]. Who knows? Perhaps, we will soon be watching Hollywood movies where the actors have been synthesized in sound and image.
Conclusion
This article summarized the changes in text-to-speech synthesis over the past few years that now enable much more natural sounding voice synthesis by computer. Unit-selection synthesis provides improved naturalness "just in time" to help push forward bleeding edge efforts in voice-enabling traditional, as well as generating completely new, telecom services.
While fulfilling the vision of a seamless integration of real-time communications and data over a single "Internet" will largely depend on creating the infrastructure that make system integration and connection of disjoint sub-systems "easy" (such as through VXML), the quality of critical infrastructure components such as the text-to-speech (TTS) system is also important. After all, as said early on in this paper, "TTS is closest to the customer's ears!"
References
[1] VoiceXML on-line tutorial at: http://www.voicexml.org/cgi-bin/vxml/tutorials/tutorial1.cgi
[2] Sondhi, M. M., and Schroeter, J., Speech Production Models and Their Digital Implementations, in: The Digital Signal Processing Handbook, V. K. Madisetti, D. B. Williams (Eds.), CRC Press, Boca Raton, Florida, pp. 44-1 to 44-21, 1997.
[3] Silverman, K., Beckman, M., Pierrehumbert, J., Ostendorf, M., Wightman, C., Price, P., and Hirschberg, J., ToBI: A standard scheme for labeling prosody. ICSLP 1992, pp. 867-879, Banff.
[4] Syrdal, A. K., Hirschberg, J., McGory, J. and Beckman, M., "Automatic ToBI prediction and alignment to speed manual labeling of prosody," Speech Communication (Special Issue: Speech annotation and corpus tools, vol. 33 (1-2), Jan. 2001, pp. 135-151.
[5] Makashay, M. J., Wightman, C. W., Syrdal, A. K. and Conkie, A., "Perceptual evaluation of automatic segmentation in text-to-speech synthesis," ISCLP 2000, vol. II, Beijing, China, 16-20 Oct. 2000, pp. 431-434.
[6] Wightman, C. W., Syrdal, A. K., Stemmer, G., Conkie, A. and Beutnagel, M., "Perceptually based automatic prosody labeling and prosodically enriched unit selection improve concatenative text-to-speech synthesis," ICSLP 2000, vol. II, Beijing, China, 16-20 Oct. 2000, pp. 71-74.
[7] Hunt, A., and Black, A., "Unit Selection in a Concatenative Speech Synthesis System Using a Large Speech Database," IEEE-ICASSP-96, Atlanta,Vol. 1. pp. 373-376, 1996.
[8] Beutnagel, M., Conkie, A., Schroeter, J., Stylianou, Y. and Syrdal, A., "The AT&T Next-Gen TTS System," Proc. Joint Meeting of ASA, EAA, and DEGA, Berlin, Germany, March 1999, available on-line at http://www.research.att.com/projects/tts/pubs.html.
[9] A.L. Gorin, G. Riccardi and J.H. Wright, "How May I Help You?", Speech Communication 23 (1997), pp. 113-127. Also see press release at http://www.att.com/press/item/0,1354,3565,00.html
[10] Schroeter, J., Ostermann, J., Graf, H. P., Beutnagel, M., Cosato, E., Syrdal, A., Conkie, A. and Stylianou, Y., "Multimodal speech synthesis," ICME 2000, New York, 2000, pp. MPS11.3.
Footnote 2: A "natural-quality" TTS system would pass the Turing test of speech synthesis in that a listener would no longer be able, within the intended application of the system, to say with certainty whether the speech heard was recorded or synthesized. (return to text)
Footnote 3: Mean Opinion Score is a subjective rating scale where "1" is "bad" and "5" is excellent.
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