IPSJ SIG Technical Report Vol.2009-SLP-77 No /7/ % unigram F 95 A Broadcast News Transcription System for Content Applicati
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- えみ わしあし
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1 % unigram F 95 A Broadcast News Transcription System for Content Appication Akio Kobayashi, 1 Takahiro Oku, 1 Shinich Homma, 1 Shoei Sato, 1 Toru Imai 1 and Tohru Takagi 1 This paper describes a new transcription system for content appication. The system archives broadcast news programs with their transcriptions and speaker tags with the aim of getting a coection of training and evauation data for acoustic and anguage modes. Bedes it is aso utiized for extracting and describing metadata for TV programs. The system has the functions of muc and speech detection during dua-gender decoding, speaker diarization, and automatic anguage mode updating for upcoming news shows. Trigram attices are compressed into confuon networks that are indexed for known item retrieva. The system achieved a 9.2 % of word error rate and a 95 of F-measure in evauation of known item retrieva for 53 Japanese broadcast news shows. 1. NHK 1) NHK 2) 3) 5) 6) 7) (Known Item Retrieva) ( 1) NHK / 1 ( 1 NHK NHK Science and Technoogy Research Laboratories 1 c 2009 Information Procesng Society of Japan
2 情報処理学会研究報告 M a etri phoneacousti cmode s W ord bi gram M ae monophone acoustic modes Phoneme bigram ph0,0 Reset after ong non-speech ph 0 muc Start of detection 0,1 : 0 0 muc Penaty between genders End of detection ph1,0 1 muc ph1,1 : 1 muc bigram Femae monophone acoustic modes 図 3 音素認識による発話区間検出と音楽検出 Fig. 3 Speech and Muc Dtection Phoneti c tree Start of recogni ti on 0 Endof recogni ti on 1 1 Phoneti c Parti a tree Gender W ord word change atti ce bi gram contro Fema etri phoneacousti cmode s W ord Audi o c ana y s Eary decion withoutput i nput Acousti andspeechdetecti on trigram rescoring Speech segment 図 4 男女並列の連続音声認識 Fig. 4 Dua-Gender Speech Decoder 内容 話者名の各情報は音声情報として統合され データベースに蓄積される また 音声 図 1 報道番組自動書き起こしシステム概要 Fig. 1 Broadcast News Transcription System 認識で得られたラティスはコンフュージョンネットワークに圧縮され 番組情報 発話時刻 4 とともに索引化してデータベースに蓄積される (図 1- ) 図 2 に示すクライアントでは ビデオ映像と同期して発話内容を閲覧したり キーワー ドを入力して発話内容の検索を行う 2.2 発話区間 音楽検出 背景音や男女の話者が混在した放送音声の自動書き起こしのための発話区間検出は フ レーム単位の細かな音声/非音声の判定よりも 多少の非音声区間を音声区間と誤ることは あっても 音声区間の欠落をできる限り抑え 音声を適度な長さの区間に切り出して 認識 率の向上に寄与することが重要である また 音声始終端検出までの遅れ時間はできる限り 小さく 音声認識に不要なテーマ曲やジングル等の音楽検出も求められる 本システムの発話区間検出は 音のパワーだけでなく周波数特性も考慮して 男女並列の 性別依存音響モデルによる音素認識をエンドレスに実行し その時の尤度から発話区間検出 および音楽検出を行う (図 3) 音素認識は 男女間遷移が可能で枝刈り共通の男女並列音素 図 2 クライアント画面 Fig. 2 Cient Appication 認識を常時実行し 累積音素尤度の比を利用して発話の始端と終端を早期に検出する8),9) 音楽の検出には まず音楽専用 HMM(6 状態 4 出力 戻り遷移あり 32 混合モデル) を 1 の音声認識は 番組 代 海外ネットワークなど) を対象にデータを収集している 図 1- 各種報道番組で放送されるテーマ曲やジングル等 46 個の音楽データ (切り出し位置を 16 通 音声から抽出された音響特徴量を入力として発話区間を検出し 音声認識結果を出力する りに拡張) から 最尤推定法で学習した この音楽専用 HMM(muc) を 無音 非音声モ 2 の話者識別は 音声認識と同様の音響特徴量を入力として 音声認識と並行して話 図 1- デル () と並列に前記男女並列音素ネットワークへ加え (図 3) 累積尤度比に基づいて発 3 の言語モデル自動更新は ウェブ上のニュースから最新のニュース 者を識別する 図 1- 話区間検出と同時に音楽区間も検出する 音楽と判定された区間の音声は 後段の男女並列 テキストを取得し 言語モデルを逐次更新する 各ブロックから出力された発話区間 発話 連続音声認識には送られず マークを音声認識結果として出力する 2 c 2009 Information Procesng Society of Japan
3 2.3 8),9) ( 4) ) BIC(Bayean Information Criterion; ) BIC 11) BIC x, y Σ N BIC(x, y) = 1 [ ] Nxy og Σxy Nx og Σx Ny og Σy αp (1) 2 Σxy x y P, α BIC x y (1) x y (1) x 1 y BIC 2.5 (NHK ) ( ) trigram trigram 12) (12.5M) ) (MPE; Minimum Phone Error) 18) 14) 15) 16),17) bigram trigram trigram 17) pivot NHK / 1 53 / 3 c 2009 Information Procesng Society of Japan
4 1 Tabe 1 Evauation Data Tabe 2 2 Overa Recognition Resuts ( ) (%) (53 ) k 105.7k k 92.9k k k 11.4k ( ) 4.1 ( 0.8%) tree exicon bigram 2 trigram MPE 18) 8) ( ) 0.5 MPE MFCC ( ) 660 (202.3M ). 60k (20 ) 2 9.2% / 5.3% ( 3) WER (%) 8.4k 95.8k 9.2 / 7.0k 85.9k k 9.9k (%) Tabe 3 Recognition Resuts (WER, %) / (%) Tabe 4 Speaker Diarization Resuts (%) DER MS FS SE NHK NHK / 5.1% 5% 19) / (FRR; Fase Rejection Rate) 21.3%(115 /540 ) (FAR; Fase Acceptance Rate) 26.0%(149 /573 ) 4 c 2009 Information Procesng Society of Japan
5 5 (%) Tabe 5 Speaker Diarization Resuts (Known Speakers, %) FRR FAR NHK NHK (unigram, %) Tabe 6 Known Item Retrieva Resuts (unigram, %) unigram( ) unigram( ) F F (bigram, %) Tabe 7 Known Item Retrieva Resuts (bigram, %) bigram( ) bigram( ) F F = 98.7% ( ) NHK (1) α NHK NHK 24 NHK NIST (2) DER(Diarization Error Rate) 20) DER DER = FS + MS + SE 100 (3) FS(Fase Speech) MS(Missed Speech) SE(Speech Error) ( 5) 4 5 NHK NHK 1 DER,FRR NHK 1 NHK NHK FAR 1 NHK 1 NHK (Known Item Retrieva) (precion) (reca) F (F-measure) unigram,bigram 20 unigram,bigram / / 6, 7 unigram = 0.5 F 95 bigram unigram F bigram = 0.5 F 84.7 / 5.3% unigram unigram F 5 c 2009 Information Procesng Society of Japan
6 4. 9.2% unigram F 95 / 1) Vo.63, No.3, pp (2008). 2),,,,,,,,,,,, CurioView :, 7-5 (2008). 3) Renas, S., Abberey, D., Kirby, D. and Robinson, T.: Indexing and Retrieva of Broadcast News, Speech Communication, Vo.32, pp.5 20 (2000). 4) Federico, M.: A System for the Retrieva of Itaian Broadcast News, Speech Communication, Vo.32, pp (2000). 5) Dowman, M., Taban, V., Cunningham, H. and Popov, B.: Web-Assted Annotation, Semantic Indexing and Search of Teevion and Radio News, Proc. the 14th Internationa Word Wide Web Conference, pp (2005). 6) PodCaste : 2.0 (2007-SLP-65) Vo.2007, No.11, pp (2007). 7) PodCaste : Web 2.0 (2007-SLP-65) Vo.2007, No.11, pp (2007). 8) 2 (2008). 9) Imai, T., Sato, S., Homma, S., Onoe, K. and Kobayashi, A.: Onine Speech Detection and Dua-Gender Speech Recognition for Captioning Broadcast News, IEICE Trans. Information and Systems, Vo.E90-D, No.8, pp (2007). 10) Liu, D. and Kubaa, F.: Fast Speaker Change Detection for Broadcast News Transcription and Indexing, Proc. EUROSPEECH 99, Vo.3, pp (1999). 11) Chen, S. and Gopaakrishnan, P.: Speaker, environment and channe change detection and custering via the Bayean information criterion, Proc. DARPA Speech Recognition Workshop, pp (1998). 12) Vo.40, No.4, pp (1999). 13) Vo , pp (2008). 14) Cheba, C. and Acero, A.: Potion specific posterior attices for indexing speech, Proc. the 43rd Annua Meeting on ACL, pp (2005). 15) Meng, S., Peng, Y., Seide, F. and Liu, J.: A Study of Lattice-Based Spoken Term Detection for Chinese Spontaneous Speech, ASRU IEEE Workshop, pp (2007). 16) Mangu, L., Bri, E. and Stocke, A.: Finding Consensus in Speech Recognition: Word Error Minimization and Other Appications of Confuon Networks, Computer Speech and Language, Vo.14, No.4, pp (2000). 17) Hakkani-Tür, D., Bechet, F., Riccardi, G. and Tur, G.: Beyond ASR 1-best: Ung Word Confuon Networks in Spoken Language Understanding, Computer Speech and Language, Vo.20, No.4, pp (2006). 18) Povey, D. and Woodand, P.: Minimum phone error and I-smoothing for improved discriminative training, Proc. ICASSP, pp.i (2002). 19) 10 2 (2006). 20) 6 c 2009 Information Procesng Society of Japan
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