SPEECH DATA ANALYSIS FOR DIPHONE CONSTRUCTION OF A MÄORI ONLINE TEXT-TO-SPEECH SYNTHESIZER

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1 SPEECH DATA ANALYSIS FOR DIPHONE CONSTRUCTION OF A MÄORI ONLINE TEXT-TO-SPEECH SYNTHESIZER Mark R. Laws Visiting Post-Doctoral Research Fellow Center for Advanced Computing and Virtual Experiments, College of Engineering, University of Hawaii, USA Abstract One of the main types of speech processing technologies today is text-to-speech (TTS) synthesis. A well established speech synthesizer technique called diphone concatenation uses a speakers processed speech examples to apply a more human-like response to the TTS synthesis system. This methodology has been used to construct many diphone databases for various languages, and was the basis for building the first Mäori diphone database. The database was then tested using a number of TTS tools. Integrating the TTS system with an existing internet based English-Mäori Word Translator has advantages for language learners [1]. The goal to assist people to hear the proper pronunciation of translated Mäori words is the prime motivation. Utilizing internet technology as the means to widely distribute the written and spoken language forms reflects the multi-disciplinary cross purpose nature of this research. Propagation of the Mäori language via the internet can assist in it s long term survival through greater external exposure, awareness and usage. This paper brings together various theoretical contributions in publications and practical applications from research projects to help construct and test a working model for synthesizing Mäori speech. Key Words text-to-speech; synthesis; diphone; database; Mäori language; translator. 1. Introduction TTS is the artificial generation of a language from one state (i.e. text) to another state (i.e. speech) under complex conditions associated with human languages and computing processes [2, 3]. These complex issues relate to linguistics, psychology, biology and acoustics, including the mathematical modeling, the structured computational programming, systems performance, and distribution [4]. Research on TTS synthesis has provided a wealth of documentation, software, tools and systems for theoretical and applied practices. High quality synthetic speech which retains intact many facets of language, linguistic, acoustic and articulatory properties to closely resemble meaningful human speech is the prime objective of researchers, developers and engineers in this field [5]. Research of late suggests that speech synthesis has been one of the fastest growing speech technologies in the computing industry [6]. Thus demand by a large cross-section of end-users to access various applications, available across all system platforms, fuels the prime objective to improve on the current technologies [7]. Many dedicated international conferences, laboratory and collaborative research teams on TTS synthesis have also contributed to that growth. All the world s major languages have been taken through some process in one form or another, to provide numerous working TTS models [2, 3, 5, 6]. 2. Mäori TTS Unfortunately, minor languages have not had the same TTS treatment as the mega-languages, especially any of the Polynesian languages, including Mäori [1, 8, 9]. Therefore, identification of the core components required for a Mäori TTS system have been recently undertaken by a small number of researchers in isolation. Small experimental projects to test a number of methods and principles have resulted. Such as the TTS conversion for Mäori [9] or the computer pronunciation training system [10], and the experimental Mäori TTS system [11]. Thus to hasten the development of a fully functional Mäori TTS synthesizer, joint collaborative research with several renowned international projects were established [4, 6, 8, 11]. This has allowed the Mäori language to be presented to the wider scientific speech community well in advance of other similar languages of the same size. The TTS synthesis technique in this research is derived from concatenating indexed digitized speech examples from a native speaker. This exemplifies the notion of applying a more natural human-like speech capability to the system. This methodology was specifically designed for the construction of the Mäori diphone database containing speech parameters for pitch and duration. 3. Mäori Diphone Analysis This section describes the methodology specifically designed for the construction of a Mäori diphone database used with the MBROLA project [3, 6, 8, 11]. A speech synthesizer based on the concatenation of diphones that reads text via a phoneme transcribed list appended with

2 parameters for pitch and duration, to reproduce the best possible synthesized output [3, 6]. The speech data analysis and the construction of a complete Mäori diphone database first began with a small test diphone experiment with the MBROLA project as described in [8, 11]. This section expands on that description to which a complete diphone database of the Mäori language was developed. This outlines the process involved with guidelines to consider before and during full construction. 4. What is a diphone? The term seems to be used only amongst the speech synthesis research fraternity, as it is not a common term used in linguistics by phoneticians or phonologists. There is no entry in the Encyclopedic Dictionary of Language and Linguistics [12] or in the Collins English Dictionary [13] to describe what the word actually means. Still, it has been best described as all the phonemic transcriptions taken between all the possible phonemes used in a particular language. For example, there are over 1,400 diphones for English, 1,200 for French, 1,800 for German and 800 for Spanish. Yet, just over 230 for Mäori [6, 8]. Thus each language has varying numbers of diphones based on the total number of possible phoneme combinations. Then as a general rule, the number of diphones in a language is the number of phonemes squared. The main feature of the diphone is the phonemic transitional boundary between two adjacent phonemes. Therefore, boundary holds the rich co-articulation information that is an important feature of natural continuous speech [3, 4, 6, 14]. Their main interest in synthesis is that they minimize concatenation problems, since they involve most of the transitions and coarticulations between phones... [6]. The current method to derive all the diphones from a speech data set is by hand segmenting the transitions between two neighboring phonemes. Starting from one phonemes stable state position (usually in the middle), across the articulation channel, to the middle of the next phonemes stable position. Silence is also classed as a consonant phoneme (e.g. labeled _ ), usually taken at the beginning and ending of the phonemes to match the silences before, between and after words; they are therefore an important unit within the diphone inventory. Segmenting all the possible diphones from any language is a time consuming process, thus preparation is the key to successfully undertaking this task. Preparing a complete inventory of the diphones took into consideration the following points. The first phase involved the transcribing all the consonant-vowel combinations of CV, VC, VV, and CC. Identifying all the diphones in Mäori included mapping all the possible combinations of vowel and consonant clusters, noting that the only CC pairs included the initial silence before all the consonants (see Table 1, 2 and 4). Note that the phonetic notion used in this paper is SAMPA, as this notation is the standard used by the MBROLA project when working with diphone databases. More details on this and other symbolic coding systems is outlined in [15]. Table 1: The Mäori diphone inventory matrix showing all the possible CV combinations. Note that these three tables are also used to identify the impossible diphone combinations that are redundant in the language, these are left blank. CV: e i A o u ei ai _ p p-e p-i p-a p-o p-u p-ei p-ai p-oi p-@u t t-e t-i t-a t-o t-u t-ei t-ai t-oi t-@u k k-e k-i k-a k-o k-u k-ei k-ai k-oi k-@u f f-e f-i f-a f-o f-u f-ai f-@u h h-e h-i h-a h-o h-u h-ei h-ai h-oi h-@u m m-e m-i m-a m-o m-u m-ei m-ai m-@u n n-e n-i n-a n-o n-u n-ei n-ai n-oi n-@u N N-e N-i N-A N-o N-u N-aI N-OI N-@U r r-e r-i r-a r-o r-u r-ei r-ai r-oi r-@u w w-e w-i w-a w-ai w-@u Table 2: The Mäori diphone inventory matrix showing all the possible VV combinations. VV: e i A o u ei ai _ e e-e ea e-o eu e-_ i i-e i-i i-a i-o i-u i-ai i-_ A A-e A-A A-o A-u A-aI A-@U A-_ o o-e o-a o-o o-u o-_ u u-e u-i ua u-o u-u u-_ ei ei-_ ai ai-e ai-i ai-a ai-o ai-_ OI OI-A @U-o _-e _-i _-A _-o _-u _-ei _-ai _-@U _-_

3 Table 3: All the possible VC combinations of the Mäori diphone inventory matrix. VC: p t k f h m n N r w e e-p e-t e-k e-f e-h e-m e-n e-n e-r e-w i i-p i-t i-k i-f i-h i-m i-n i-n i-r i-w A A-p A-t A-k A-f A-h A-m A-n A-N A-r A-w o o-p o-t o-k o-f o-h o-m o-n o-n o-r o-w u u-p u-t u-k u-f u-h u-m u-n u-n u-r u-w ei ei-t ei-k ei-h ei-m ei-n ei-n ei-r ai ai-p ai-t ai-k ai-f ai-h ai-m ai-n ai-n ai-r ai-w OI OI-p OI-t OI-k OI-m OI-n @U-w -p _-t _-k _-f _-h _-m _-n _-N _-r _-w The second part of the diphone inventory process was to compile a set of Mäori words that each contained a predetermined diphone. Therefore, a list of words was created (see Table 4) [8]. The words used for the inventory (or corpus) were randomly selected from the English-Mäori Word Translator database [1]. As long as each selected word contained one of the diphones which was derived from the above three consonant and vowel combinations tables, it was accepted. It was decided that as there were 230 diphones (including silence), as a minimum, there would be 230 separate words assigned for each individual diphone. There are cases where many of the single words contained more than one diphone, but to reduce confusion the one word for one diphone criteria upheld. Table 4: Examples of the Mäori word inventory with associated diphones and recorded speech files. Word Left Right Word Number: Phoneme: Phoneme: Utterance: Wave Files: 1 Silence ##2216.wav 2 auahi #@u2216.wav 3 _ A a #a2216.wav 35 A _ huia a#2216.wav 36 whakaauraki a@u2216.wav 37 A A a aa2216.wav 142 n A na na2216.wav 143 N A nga Nga2216.wav 144 n ai naihi nai2216.wav 228 w ai wai wai2216.wav 229 w e wero we2216.wav 230 w i wiki wi2216.wav The third phase required the 230 selected Mäori words to be recorded as speech files in an environment that provided very good acoustics low background noise with no reverberation or echo, using high quality industry standard sound equipment (e.g. Digital-to-Analogue tape technology). The native speaker first practiced the intelligibly and pronunciation of all the words. The next step required the speaker to pronounce all the words with a constant pitch this was to enhance the analysis resynthesis procedure undertaken by MBROLA [6]. To maintain constant pitch throughout the entire session, the recording of a single vowel utterance (e.g. A ) on a separate recorder was used as an audible reference. This was played at the beginning of every fifth word utterance to assist the speaker to keep at a steady pitch. The speaker had to speak very intelligibly, but not too slowly so the co-articulation between the phonemes was not lost. Because Mäori vowels are very stable in all positions within a word (e.g. initial, medial and final), there were no severe co-articulation problems encountered. It was also very important to only use one recording session, this is to be consistent with the equalization steps involved with the segmentation process. The speech utterances were saved directly to DAT at 48 khz 16 bit mono. Once the recording session was completed, the speech was transferred as a computer readable file. The file was processed for normalization and then down-sampled to 22,050 Hz 16 bit mono. The file was then divided into the many word labeled units and saved in separate manageable wave files. This made the diphone segmentation process an easier task to perform. The actual segmentation and labeling of the diphones was the painstaking part of this work. Figure 1 shows how the diphones were processed using an application called Diphone Studio, which was specifically designed to assist with the construction of diphone databases for MBROLA [16]. Hand segmentation of each file with three sampling points was undertaken, indicating the left, the middle and right boundaries. These three boundaries represent the start of the first phoneme sample, the crossover point between the two phonemes and the end of the second phoneme sample (see Table 5).

4 Figure 1: An example of the diphone unit A-f being segmented from the word awhina, the sampling points are shown in milliseconds. Table 5: Examples of the Mäori diphone data text file generated by the Diphone Studio segmentation tool. Note, a full listing is published in [8]. Diphone File: Diphone Label: Left Boundary: Right Boundary: Middle Boundary: d0.raw _-_ d1.raw _-@U d2.raw _-A d100.raw h-ei d101.raw h-i d102.raw h-o d227.raw w-ai d228.raw w-e d229.raw w-i To monitor the quality of the diphone segmentation, the pitch and the energy, each diphone was checked by testing it with a number of words/phrases (e.g. A haka mana para tawaa NA fa). This was an effective way to monitor the level and tone each diphone should not vary too far from the pitch and energy. This first evaluation step only gave a very crude reproduction of the speech, but in terms of checking all adjoining diphones, it was very successful, the diphones performed well within the required specifications from MBROLA and the Diphone Studio documentation, including an assurance from the native speaker. Post-processing of all the diphones took place with the entire inventory being compiled into; i) A data text file containing all the diphone details; ii) Each diphone was extracted from their original word examples and saved in their.raw format (e.g. headerless wave files). The files contain no other linguistic information. The diphone database was then sent to the MBROLA project team for further processing, where the data analysis and re-synthesis procedures were performed, then the compiled database was returned for testing and evaluation. 5. Testing the Mäori Diphone Database The compiled (or binary) Mäori diphone database named mb1 was initially tested using the MBROLIGN speech analysis and synthesis tool [6]. MBROLIGN is a speech and phonetic alignment program that generates files based on prosody (i.e. mainly pitch and duration). This tool can automatically extract prosodic features from digitized speech examples and aligns the phoneme labels from the text example with pitch and duration vectors. These prosodic files can be read, and by calling the correct diphones, it produces the appropriate high quality speech synthesis output [6]. This tool is ideal for testing all the

5 diphones in the database with various diphone-to-diphone combinations including manual adjustments to the prosodic vectors to fine tune the speech outputs for each utterance. For example, Figure 2 and Table 6 illustrate the process involved to test all the diphones required from the database to construct the phrase kia ora. First a speech file and the phonetic transcription of the diphones are loaded, then a synthesized version of the phrase is generated and saved as an output file along with the prosodic file containing the duration and pitch points. The prosodic information for each diphone will contain its duration in milliseconds, and an optional series of pitch pattern points composed of two floating integers, the position of the pitch pattern point within the diphone (in percentage of its total duration), and the pitch value (in Hz) at this position [14]. Figure 2: The MBROLIGN test toolbox used for the phrase kia ora, showing the original speech file example (kiaora.wav), the phonetic transcription, the synthesised output file (kiaoras.wav) and the pitch rise/fall analysis. Table 6: The phrase kia ora requires the following diphone examples and prosodic features. MBROLIGN generates parameters for each diphone based on the pitch analysis of the original speech file example and saves these in an output file (kiaora.pho). Diphone Diphone Duration Pitch Point-1 Pitch Value-1 Pitch Point-2 Pitch Value-2 Name: Units: (msec) (%) (Hz) (%) (Hz) -_ k _-k i k-i A i-a _ A-_ o _-o r o-r A r-a _ A-_ These initial test results were very acceptable, given the duration and pitch pattern points were adjusted to compensate for slight variations in the quality of some diphone units. The speech synthesis output wave files all had a very close resemblance to the original Mäori speaker s voice. This represents a very good reproduction that far exceeds all other attempts made with the current TTS synthesis tools to pronounce Mäori with the correct intonation [8].

6 Although the MBROLA synthesizer implements a crossplatform working system that can access many diphone databases to synthesize those languages with high quality speech, there is one setback with this system. It is not a complete TTS package, as every word, phrase or sentence requires a prosodic file to be generated before it can be synthesized. This means a text-to-phonetic processing system is also required to enable the automatic generation of the prosodic commands. 6. Festival Speech Synthesis System The Festival Speech Synthesis System [17] integrates with MBROLA to deliver a complete TTS system ready for installation with other applications such as the English-Mäori Online Word Translator [1]. MBROLA and its many users prefer to use the Festival system because it offers full TTS compatibility with their current diphone databases. Another feature that Festival now offers is the option to build new voices based on either an already supported language or a new language [18]. Experiments to build a Festival diphone database with the existing Mäori segmented and labeled diphones has already begun [8] 7. Conclusion Fundamental computational linguistics provided the model to build a working diphone database for a TTS system based on the MBROLA project. By utilizing a variety of specialized tools to create, analyze then test the collection of customized files and scripts sped up the developmental cycle. Finally, by utilizing a team of local and international researchers to provide the technical assistance when appropriate or when requested, was extremely valuable. It is apparent that this entire process of building a functional TTS system for Mäori has opened up further research issues that need to be addressed in the near future. Important issues associated with advancing the prosodic parameters of duration and pitch, with appropriate tokens for breaks, stress placement, syllable prediction, intonation and lexical rules. All these are rich in the domain of computational linguistic problem solving tasks [19]. The construction of the Mäori diphone database is the first step towards achieving this. 8. Acknowledgements The New Zealand Foundation for Research Science and Technology, for the Connectionist-based Intelligent Information Systems Grants (OU0808 and AUTX0201) and the Post-Doctoral Science and Technology Fellowship (AITX0205). References [1] M. Laws, R. Kilgour, M. Watts, Analysis of the New Zealand and Mäori On-Line Translator, Proceedings of the Fifth Joint Conference on Information Sciences, Atlantic City, NJ. USA, [2] B. Möbius, J. Schroeter, J. Santen, R. Sproat, J. Olive, Recent Advances in Multilingual Text-To-Speech Synthesis, AT&T Bell Laboratories, Murray Hill, New Jersey, [3] T. Dutoit, An introduction to text-to-speech synthesis Dordrecht, Kluwer Academic Publishers, [4] R. Sproat, Multilingual Text Analysis for Text-to- Speech Synthesis. 12th European Conference on Artificial Intelligence [5] J.P.H. van Santen, R. Sproat, J. Olive (Eds) Speech synthesis Heidelberg, Springer Verlag, [6] T. Dutoit, V. Pagel, N. Pierret, F. Bataille, O. Van Der Vrecken, The MBROLA Project: Towards a Set of High- Quality Speech Synthesizers Free of Use for Non- Commercial Purposes, ICSLP 96, Philadelphia, [7] D.G. Stork, HAL'S legacy: 2001's computer as dream and reality Cambridge Massachusetts, The MIT Press, [8] M.R. Laws, (2001) Mäori Language Integration in the Age of Information Technology: A Computational Approach. Doctor of Philosophy Thesis, University of Otago, Dunedin, New Zealand. [9] K.P.H. Sullivan, Text-to-speech conversion for Mäori: a choice of methods?, Department of Phonetics, Umeå University, [10] P. Kingi, (1992) Te whakahuatia reo Mäori/A Mäori speech training system. Unpublished BSc Dissertation Thesis, University of Waikato, New Zealand. [11] M.R. Laws, Development of a Mäori Database for Speech Perception and Generation, 5th Joint Conference on Information Sciences, Atlantic City, NJ, [12] D. Crystal, An encyclopedic dictionary of language and linguistics London, Penguin, [13] P. Hanks, T.H. Long, L. Urdang (Eds), Collins dictionary of the English language: an extensive coverage of contemporary international and Australian English Collins, Sydney, [14] F. Malfrére, T. Dutoit, Speech Synthesis for Textto-Speech Alignment and Prosodic Feature Extraction. Proceedings of the International Symposium on Circuits and Systems, 1997, [15] J. Wells, SAMPA: Computer readable phonetic alphabet, Department of Phonetics and Linguistics, University College London, [16] A. Dirksen, L Menert, Diphone Studio Manual, Utrecht, Netherlands, [17] A. Black, P. Taylor, R. Caley, The Festival Speech Synthesis System, Centre for Speech Technology Research, University of Edinburgh, Scotland, [18] A. Black, K. Lenzo, Building Voices in the Festival Speech Synthesis System, Carnegie Mellon University, [19] R. Sproat, Algorithms for Speech Recognition and Language Processing: Part III, Finite State Methods in Language Processing, COLING'96. >

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