EP1184839A2 - Conversion graphème-phonème - Google Patents

Conversion graphème-phonème Download PDF

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Publication number
EP1184839A2
EP1184839A2 EP01117869A EP01117869A EP1184839A2 EP 1184839 A2 EP1184839 A2 EP 1184839A2 EP 01117869 A EP01117869 A EP 01117869A EP 01117869 A EP01117869 A EP 01117869A EP 1184839 A2 EP1184839 A2 EP 1184839A2
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EP
European Patent Office
Prior art keywords
grapheme
word
interface
partial words
words
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP01117869A
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German (de)
English (en)
Other versions
EP1184839B1 (fr
EP1184839A3 (fr
Inventor
Horst-Udo Hain
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Siemens Corp
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Siemens AG
Siemens Corp
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Publication of EP1184839A2 publication Critical patent/EP1184839A2/fr
Publication of EP1184839A3 publication Critical patent/EP1184839A3/fr
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Publication of EP1184839B1 publication Critical patent/EP1184839B1/fr
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination

Definitions

  • the invention relates to a method, a computer program product, a data carrier and a computer system for grapheme-phoneme conversion of a word that as a whole is not is contained in a pronunciation dictionary.
  • Speech processing methods in general are, for example from US 6 029 135, US 5 732 388, DE 19636739 C1 and DE 19719381 C1 known.
  • a speech synthesis system that is Font-to-speech or grapheme-phoneme conversion of the crucial words. Error with Sounds, syllable borders and the emphasis on words can be heard directly, can lead to incomprehensibility and at worst Case even distort the meaning of a statement.
  • a remedy can be a morphological decomposition in this case create.
  • a word that is not found in the dictionary is into its morphological components such as prefixes, stems and Suffixes are broken down and these components are searched for in the lexicon.
  • a morphological decomposition is just about long words problematic because of the number of possible The word length increases. It also requires an excellent knowledge of the word formation grammar of a Language.
  • words that are not in a pronunciation dictionary can be found using out-of-vocabulary methods (OOV method), e.g. with neural networks, transcribed.
  • OOV method out-of-vocabulary methods
  • results as the phonetic conversion of whole words with the help of a pronunciation dictionary.
  • the word can also be broken down into partial words.
  • the partial words can be pronounced with the help of a pronunciation dictionary or an OOV procedure.
  • the found Partial transcriptions can be linked together. However, this leads to errors at the separation points between the Part transcriptions.
  • the object of the invention is to join together To improve partial transcriptions. This task is accomplished by a method, a computer program product, a data carrier and a computer system according to the independent claims.
  • the computer program is called a computer program product understood as a tradable product, in what form whatever, e.g. on paper, on a computer-readable data medium, distributed over a network, etc.
  • the transcriptions of the partial words are lined up one after the other, with at least one interface between the transcriptions of the partial words.
  • the at least an interface-bounding phoneme of the partial words be determined.
  • the Grapheme-phoneme conversion of the graphemes in the context of the respective Interface using a neural network new be calculated.
  • a pronunciation lexicon has the advantage of to deliver "correct" transcription. However, it fails when unknown words occur.
  • Neural networks can provide a transcription for any character string, but may make significant mistakes.
  • the invention combines the security of the lexicon with the flexibility of neural networks.
  • the transcription of the partial words can be done in different ways take place, e.g. with an out-of-vocabulary treatment (OOV treatment).
  • OOV treatment out-of-vocabulary treatment
  • a fairly reliable way is for the word in a database, the phonetic transcriptions of words contains to search for partial words. As Transcription is then made for one found in the database Partial word the phonetic recorded in the database Transcription chosen. For most words or Subwords for useful results.
  • the word next to the found subword is at least one has another component that is not in the database can be registered using OOV treatment be transcribed phonetically.
  • OOV treatment can be done using a statistical method, e.g. by means of a neural Network, or rule-based.
  • the word is advantageously divided into partial words of a certain Minimum length disassembled so that the largest possible partial words are found and there will be little reworking.
  • FIG. 1 shows a grapheme-phoneme conversion of a word suitable computer system.
  • This assigns a processor (processor, CPU) 20, a working memory (RAM) 21, a program memory (program memory, ROM) 22, a hard disk controller (hard disc controller, HDC) 23, which is a hard drive (hard disk) 30 controls, and an interface controller (I / O controller) 24 on.
  • Processor 20, memory 21, program memory 22, hard disk controller 23 and interface controllers 24 are on a bus that CPU bus 25, for exchanging data and commands with one another coupled.
  • the computer also has an input / output bus (I / O bus) 26 on the various input and output devices couples with the interface controller 24.
  • To the Input and output devices count e.g. a general Input and output interface (I / O interface) 27, a display device (display) 28, a keyboard (keyboard) 29 and a mouse 31.)
  • the remaining gaps in the preferred embodiment closed by a neural network.
  • the task of filling in the gaps is easier, because at least the left phoneme context is assumed to be safe can be, since it comes from the pronunciation dictionary comes.
  • the input of the previous phonemes stabilized thus the output of the neural network for the one to be filled Gap, because the phoneme to be generated not only from the letters, but also depends on the previous phoneme.
  • the ending ⁇ -ig> at the end of the syllable is spoken like [IC] in the phonetic spelling SAMPA, so like [I] (untensioned short non-rounded front vowel) followed by the first sound [C] (voiceless palatal fricative).
  • the prefix ⁇ er-> is spoken like [Er], with an [E] (untensioned short unrounded semi-open front vowel, open "e") and one [r] (central sonorant).
  • Remedy can be created here by using a neural Net calculated the last according to the left transcription. there the question arises, which letters at the end of the left transcription used to determine the last sound should be.
  • a special pronunciation dictionary is used for this decision used.
  • the special feature of this lexicon is that it contains information about which grapheme group for which Heard out loud. How to create the lexicon is in Horst-Udo Hain: "Automation of the Training Procedures for Neural Networks Performing Multi-Lingual Grapheme to Phoneme Conversion ". Eurospeech 1999, pp. 2087-2090.
  • the neural network can now use the existing one right context ⁇ wise> new about phoneme and syllable boundary decide at the end of the word.
  • the result in this case is that Phoneme [g] in front of which a syllable limit is set.
  • the syllable boundary is in the right place and the ⁇ g> is also transcribed as [g] and not as [C].
  • the first according to the right transcription is redetermined using the same scheme.
  • the correct transcription for ⁇ er-> from ⁇ weise> at this point is [6] and not [Er].
  • Two sounds have to be revised here, which is why in the preferred embodiment two sounds are always revised.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Machine Translation (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
EP01117869A 2000-08-31 2001-07-23 Conversion graphème-phonème Expired - Lifetime EP1184839B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10042944A DE10042944C2 (de) 2000-08-31 2000-08-31 Graphem-Phonem-Konvertierung
DE10042944 2000-08-31

Publications (3)

Publication Number Publication Date
EP1184839A2 true EP1184839A2 (fr) 2002-03-06
EP1184839A3 EP1184839A3 (fr) 2003-02-05
EP1184839B1 EP1184839B1 (fr) 2005-09-28

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP01117869A Expired - Lifetime EP1184839B1 (fr) 2000-08-31 2001-07-23 Conversion graphème-phonème

Country Status (3)

Country Link
US (1) US7107216B2 (fr)
EP (1) EP1184839B1 (fr)
DE (2) DE10042944C2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1315108C (zh) * 2004-03-17 2007-05-09 财团法人工业技术研究院 对易标错形素重新评分以提高准确率的文字转音标的方法
CN105590623A (zh) * 2016-02-24 2016-05-18 百度在线网络技术（北京）有限公司 基于人工智能的字母音素转换模型生成方法及装置

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DE50107556D1 (de) 2005-11-03
US20020046025A1 (en) 2002-04-18
DE10042944A1 (de) 2002-03-21
US7107216B2 (en) 2006-09-12
EP1184839A3 (fr) 2003-02-05

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