JP5538415B2 - 多感覚応用音声検出 - Google Patents
多感覚応用音声検出 Download PDFInfo
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Description
本出願は、参照により本明細書に組み込まれている、2008年11月10日に出願した米国仮出願第61/113,061号、名称「Multisensory Speech Detection」の優先権を主張するものである。
(1) p(x_aud, x accel, x_prox | EPP) p (EPP)
を表すことができる。
式(1)において、x_audは音響特徴ベクトルを表し、x_accelは加速度特徴ベクトルを表し、x_proxは近接特徴ベクトルを表すものとしてよい。隠れ状態変数EPPは、エンドポインタ音声EPと姿勢状態変数Poseとの外積を表すものとしてよい。EPおよびPose変数は、離散確率変数とすることができる。
(2) p(x_aud | EP, Pose) p(x accel | EP, Pose)p(x_prox | Pose)p(EP)p(Pose)
のように分解できる。
場合によっては、分布p(x_aud, x_accel | EP, Pose)およびp(x_aud, x_accel | EP, Pose)およびp (x_prox | Pose)はガウス混合モデルとすることができる。
(3) AL = 20 * log10(RMS)
によって決定することができる。
ここで、xtは、時刻tにおける音響サンプル値とすることができる。
式(3)のように、xtは、時刻tにおける音響サンプル値とすることができ、NLは、雑音レベルの推定値とすることができる。
(5) L=α(AL)+β(ALSNR)
として決定することができる。
この式において、αおよびβは、背景雑音と信号対雑音比をスケーリングできる変数であるものとしてよい。例えば、αは、デシベル値を表すように音響サンプルのフレームのRMSレベルをスケーリングすることができる(例えば、100dbが音響のフレームのフルスケールRMSレベルに等しくなるように)。βも、同様に、信号対雑音比をスケーリングするために使用することができる。
(6) NL=(α*NL)+((1-α)*RMS)
(7) SL=(α*NL)+((1-α)*2RMS)
を使用して設定することができる。
式(6)および(7)において、RMSは、音響サンプルのRMSレベルとすることができ、αは、雑音または音声の前の推定値と現在の推定値との比である。この比は、最初に0に設定し、
(8) NL=(UpdateRateNL*NL)+(UpdateRateRMS*RMS)
に従って雑音レベルを調整することができる。
式(7)と同様に、RMSは、音響サンプルのRMSレベルとすることができる。場合によっては、UpdateRateNLとUpdateRateRMSとの和は1に等しいものとすることができる。雑音レベルが音響サンプルのRMSレベルより小さい場合、UpdateRateNLは0.995、UpdateRateRMSは0.005であってもよい。雑音レベルが音響サンプルのRMSレベルより大きい場合、式(8)を使用して雑音レベルを調整することができるが、UpdateRateNLは0.95、UpdateRateRMSは0.05であってもよい。
(9) SL=(UpdateRateSL*SL)+(UpdateRateRMS*RMS)
に従って音声レベルを調整することができる。
「Assessing Local Noise Level Estimation Methods: Application to Noise Robust ASR」、Christophe Ris、Stephane Dupont. Speech Communication、34 (2001年) 141〜158頁、「DySANA: Dynamic Speech and Noise Adaptation for Voice Activity Detection」、Ron J. Weiss、Trausti Kristjansson、ICASSP 2008年、
「Noise estimation techniques for robust speech recognition」、H.G. Hirsch、C Ehrlicher、Proc. IEEE Internat. Conf. Audio、Speech Signal Process、v12 i1、59〜67頁、および「Assessing Local Noise Level Estimation Methods」、Stephane Dupont、Christophe Ris、Workshop on Robust Methods For Speech Recognition in Adverse Conditions (Nokia、COST249、IEEE)、115〜118頁、Tampere、Finland、1999年5月。
105 ユーザー
110 モバイルデバイス
115 電話姿勢
120 PDA姿勢
125 トランシーバー姿勢
200 ブロック図
205 モバイルデバイス
207 画面
209 物理的キーパッド
211 トラックボール
213 加速度計
215 近接センサー
217 マイクロホン
219 カメラ
221 音声検出器
223 話者識別器
225 ジェスチャー分類器
227 姿勢識別器
229 スピーチエンドポインタ
231 メモリ
233 中央演算処理装置、プロセッサ
235 I/Oインターフェイス
240 インターネット
245 リモートコンピューティングデバイス
1511 新着メールインジケータ
1512 アクティブ呼インジケータ
1514 データ規格インジケータ
1515 信号強度インジケータ
1516 電池残量インジケータ
1517 クロック
1519 ウェブブラウザアプリケーションアイコン
1520 電話アプリケーションアイコン
1521 検索アプリケーションアイコン
1522 連絡先アプリケーションアイコン
1524 地図表示アプリケーションアイコン
1525 電子メールアプリケーションアイコン
1526、1527、1529 キー
1530 呼確立キー
1531 呼終了キー
1532 ドロップダウンメニューキー
1534 バックワードナビゲーションキー
1535 お気に入りキー
1536 ホームページキー
1900 コンピュータデバイス
1950 モバイルコンピュータデバイス
1902 プロセッサ
1904 メモリ
1906 ストレージデバイス
1908 高速インターフェイス
1910 高速拡張ポート
1912 低速インターフェイス
1914 低速バス
1916 ディスプレイ
1920 標準サーバー
1922 ラップトップコンピュータ
1924 ラックサーバーシステム
1950 デバイス
1952 プロセッサ
1954 ディスプレイ
1956 ディスプレイインターフェイス
1958 制御インターフェイス
1960 オーディオコーデック
1962 外部インターフェイス
1964 メモリ
1966 通信インターフェイス
1968 トランシーバー
1970 GPS(全地球測位システム)受信機モジュール
1972 拡張インターフェイス
1974 拡張メモリ
1980 携帯電話
1982 スマートフォン
Claims (19)
- コンピュータで実施される方法であって、
プロセッサを使用して、モバイルデバイスの向きを判定するステップと、
前記プロセッサを使用して、前記モバイルデバイスの前記判定された向きに基づいて前記モバイルデバイスの動作モードを決定するステップと、
前記モバイルデバイスの前記決定された動作モードに基づく音声検出パラメータを識別するステップと、を含み、前記識別された音声検出パラメータは、音声検出が終了する時を指定するための1つまたは複数の音声エネルギー閾値を定義し、
前記方法は、
検出された聴覚情報と、前記モバイルデバイスの決定された前記動作モードに基づいて識別された前記音声検出パラメータとの比較に基づいて前記モバイルデバイスのユーザーからの音声の終了を検出するステップを含む、方法。 - 前記モバイルデバイスの前記向きを判定するステップは、前記モバイルデバイスの角度を検出するステップを含む、請求項1に記載の方法。
- 前記モバイルデバイスの前記向きを判定するステップは、前記モバイルデバイスの前記ユーザーへの前記モバイルデバイスの近接度を検出するステップを含む、請求項1に記載の方法。
- 前記モバイルデバイスの前記決定された動作モードは、パーソナルデジタルアシスタント動作モード、電話動作モード、またはトランシーバー動作モードのうちの1つで構成される、請求項1に記載の方法。
- 前記モバイルデバイスの前記動作モードを決定するステップは、前記モバイルデバイスの移動を識別するように、ベイジアンネットワークを使用するステップを含む、請求項1に記載の方法。
- 前記モバイルデバイスの前記動作モードを決定するステップは、前記モバイルデバイスの移動を識別するように、隠れマルコフモデルを使用するステップを含む、請求項1に記載の方法。
- 前記モバイルデバイスの前記ユーザーに対して音声検出が開始または終了したことを示すステップをさらに含む、請求項1に記載の方法。
- 前記モバイルデバイスの前記ユーザーに音声検出が開始または終了したことを示すステップは、視覚的もしくは聴覚的通知を含む、請求項7に記載の方法。
- 1つまたは複数のコンピュータを備えたシステムであって、
前記コンピュータは、
モバイルデバイスの向きを検出する少なくとも1つのセンサーと、
前記モバイルデバイスの前記検出された向きに基づいて前記モバイルデバイスの姿勢を識別する姿勢識別器と、
前記モバイルデバイスの識別された姿勢に基づく、選択された音声検出パラメータを識別するスピーチエンドポインタとを有し、前記選択された音声検出パラメータは、音声検出が終了する時を指定するための1つまたは複数の音声エネルギー閾値を定義する、システム。 - 前記少なくとも1つのセンサーは、加速度計を備える、請求項9に記載のシステム。
- 前記少なくとも1つのセンサーは、近接センサーを備える、請求項9に記載のシステム。
- 前記モバイルデバイスの移動を分類するジェスチャー分類器をさらに備える、請求項9に記載のシステム。
- 前記識別される姿勢は、パーソナルデジタルアシスタント姿勢、電話姿勢、またはトランシーバー姿勢のうちの1つで構成される、請求項9に記載のシステム。
- 1つまたは複数のコンピュータを備えたシステムであって、
前記コンピュータは、
モバイルデバイスの向きを検出する少なくとも1つのセンサーと、
前記モバイルデバイスの前記検出された向きに基づいて前記モバイルデバイスの姿勢を識別する姿勢識別器と、
前記モバイルデバイスの識別された姿勢に基づく、選択された音声検出パラメータを識別する手段とを有し、前記音声検出パラメータは、前記モバイルデバイスのユーザーが前記モバイルデバイスに対する発声を終了したかどうかを判定するための1つまたは複数の音声エネルギー閾値を定義する、システム。 - 前記少なくとも1つのセンサーは、近接センサーを備える、請求項14に記載のシステム。
- 前記識別される姿勢は、パーソナルデジタルアシスタント姿勢、電話姿勢、またはトランシーバー姿勢のうちの1つで構成される、請求項14に記載のシステム。
- 前記モバイルデバイスの移動を分類するジェスチャー分類器をさらに備える、請求項14に記載のシステム。
- 前記少なくとも1つのセンサーは、カメラを備える、請求項14に記載のシステム。
- 前記少なくとも1つのセンサーは、加速度計を備える、請求項14に記載のシステム。
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