TWI585745B - 用於在一數位助理中處理語音之方法，用於處理語音之電子器件，及用於處理語音之電腦可讀儲存媒體 - Google Patents

用於在一數位助理中處理語音之方法，用於處理語音之電子器件，及用於處理語音之電腦可讀儲存媒體 Download PDF

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TWI585745B
TWI585745B TW104128223A TW104128223A TWI585745B TW I585745 B TWI585745 B TW I585745B TW 104128223 A TW104128223 A TW 104128223A TW 104128223 A TW104128223 A TW 104128223A TW I585745 B TWI585745 B TW I585745B
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input
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voice input
voice
speech
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TW201621882A (zh
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馬赫許 克里斯納穆西
馬西亞斯 保利克
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蘋果公司
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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
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • 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
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • 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
    • G10L15/00—Speech recognition
    • G10L15/01—Assessment or evaluation of speech recognition systems
    • 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
    • G10L15/00—Speech recognition
    • G10L15/28—Constructional details of speech recognition systems
    • G10L15/32—Multiple recognisers used in sequence or in parallel; Score combination systems therefor, e.g. voting systems
    • 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
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • G10L2015/025—Phonemes, fenemes or fenones being the recognition units

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
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  • Acoustics & Sound (AREA)
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  • Computer Vision & Pattern Recognition (AREA)
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  • Machine Translation (AREA)

Description

用於在一數位助理中處理語音之方法，用於處理語音之電子器件，及用於處理語音之電腦可讀儲存媒體 相關申請案之交叉引用
本申請案主張2014年8月28日申請之名為「AUTOMATIC SPEECH RECOGNITION BASED ON USER FEEDBACK」之美國臨時案第62/043,041號及2015年1月7日申請之名為「AUTOMATIC SPEECH RECOGNITION BASED ON USER FEEDBACK」之美國非臨時案第14/591,754號之優先權，該等臨時案在此出於所有目的以全文引用之方式併入。
本發明大體上係關於自動語音辨識，且更具體而言係關於基於使用者回饋改善自動語音辨識。
自動語音辨識(ASR)系統可能遭受轉錄錯誤。此等錯誤可由於多種原因而出現，諸如，錯亂的語音輸入，具有吵雜背景之語音輸入或含有在發音上類似於其他字之字的語音輸入。此外，在即時ASR系統中，可實施精確性折衷以達成可接受的延時時間。舉例而言，可實施較小詞彙模型或較不穩固之語音辨識引擎。此等折衷可造成轉錄錯誤。習知地，可相同地處理由ASR系統接收之每一語音輸入。然而，相同地處理所有語音輸入可導致類似轉錄錯誤反覆地再發生，其可就使用者而言導致挫敗感及不良使用者體驗。
本發明提供用於在一數位助理中處理語音之系統及處理程序。在實例處理程序中，可接收來自使用者之第一語音輸入。可使用第一自動語音辨識系統處理該第一語音輸入以產生第一辨識結果。可接收指示該第一辨識結果中之潛在錯誤之輸入。該輸入可用於改善該第一辨識結果。
在一些實例中，該輸入可包括為第一語音輸入之重複的第二語音輸入。可使用第二自動語音辨識系統處理該第二語音輸入以產生第二辨識結果。
在一些實例中，可提示使用者重複第一語音輸入之至少一部分。可自使用者輸入接收為該第一語音輸入之重複的第三語音輸入。可使用第二自動語音辨識系統處理第三語音輸入以產生第三辨識結果。
在一些實例中，可使用第二自動語音辨識系統處理第一語音輸入以產生第四辨識結果。
100‧‧‧系統
102‧‧‧數位助理用戶端
102a‧‧‧用戶端側部分
102b‧‧‧用戶端側部分
104‧‧‧使用者器件
104a‧‧‧使用者器件
104b‧‧‧使用者器件
106‧‧‧伺服器側部分/數位助理伺服器
108‧‧‧伺服器系統
110‧‧‧網路
112‧‧‧面向用戶端之I/O介面
114‧‧‧處理模組
116‧‧‧資料及模型
118‧‧‧至外部服務之I/O介面
120‧‧‧外部服務
202‧‧‧記憶體介面
204‧‧‧處理器
206‧‧‧周邊器件介面
210‧‧‧運動感測器
212‧‧‧光感測器
214‧‧‧近接感測器
216‧‧‧感測器
220‧‧‧攝影機子系統
222‧‧‧光學感測器
224‧‧‧通信子系統
226‧‧‧音訊子系統
228‧‧‧揚聲器
230‧‧‧麥克風
240‧‧‧輸入/輸出子系統
242‧‧‧觸控式螢幕控制器
244‧‧‧輸入控制器
246‧‧‧觸控式螢幕
248‧‧‧輸入/控制器件
250‧‧‧記憶體
252‧‧‧作業系統
254‧‧‧通信模組
256‧‧‧使用者介面模組
258‧‧‧感測器處理模組
260‧‧‧電話模組
262‧‧‧應用程式模組
264‧‧‧數位助理用戶端模組
266‧‧‧使用者資料
300‧‧‧數位助理系統
302‧‧‧記憶體
304‧‧‧處理器
306‧‧‧輸入/輸出(I/O)介面
308‧‧‧網路通信介面
310‧‧‧信號線
312‧‧‧有線通信埠
314‧‧‧無線傳輸及接收電路
316‧‧‧輸入/輸出器件
318‧‧‧作業系統
320‧‧‧通信模組
322‧‧‧使用者介面模組
324‧‧‧應用程式
326‧‧‧數位助理模組
328‧‧‧輸入/輸出處理模組
330‧‧‧語音轉文字處理模組
331‧‧‧語音字母轉換模組
332‧‧‧自然語言處理模組
334‧‧‧對話流程處理模組
336‧‧‧任務流程處理模組
338‧‧‧服務處理模組
344‧‧‧詞彙
348‧‧‧使用者資料
354‧‧‧任務流程模型
356‧‧‧服務模型
360‧‧‧本體
400‧‧‧處理程序
402‧‧‧區塊
404‧‧‧區塊
406‧‧‧區塊
408‧‧‧區塊
410‧‧‧區塊
412‧‧‧區塊
414‧‧‧區塊
416‧‧‧區塊
418‧‧‧區塊
420‧‧‧區塊
422‧‧‧區塊
424‧‧‧區塊
426‧‧‧區塊
428‧‧‧區塊
430‧‧‧區塊
432‧‧‧區塊
500‧‧‧電子器件
502‧‧‧觸控式螢幕顯示器單元
504‧‧‧音訊輸入單元
506‧‧‧輸入單元
508‧‧‧揚聲器單元
510‧‧‧處理單元
512‧‧‧接收單元
514‧‧‧語音處理單元
516‧‧‧判定單元
518‧‧‧執行單元
520‧‧‧提示單元
522‧‧‧識別單元
圖1說明根據各種實例之用於實施數位助理之系統及環境。
圖2說明根據各種實例之實施數位助理之用戶端側部分之使用者器件。
圖3A說明根據各種實例之數位助理系統或其伺服器部分。
圖3B說明根據各種實例之圖3A中所示的數位助理之功能。
圖4A至圖4B說明根據各種實例之用於處理語音之處理程序。
圖5說明根據各種實例之電子器件之功能方塊圖。
在實例之以下描述中，參考藉助於說明展示可實踐之特定實例的附圖。應理解，在不脫離各種實例之範疇之情況下，可使用其他實 例，且可進行結構改變。
如上文所描述，來自ASR系統之類似錯誤之重複重現可導致不良的使用者體驗。在本文所描述之各種實例，提供用於基於使用者回饋改善語音處理之系統及處理程序。在一些實例中，可在數位助理中執行語音處理。在一項實例處理程序中，可接收來自使用者之第一語音輸入。可使用第一ASR系統處理第一語音輸入以產生第一辨識結果。可接收指示第一辨識結果中之潛在錯誤之輸入。該輸入可用以產生經改善之辨識結果，從而減少類似錯誤重現之機率。
在一些實例中，該輸入可包括為第一語音輸入之重複的第二語音輸入。具體而言，使用者可重複第一語音輸入來指示第一辨識結果中之潛在錯誤。可使用第二ASR系統處理第二語音輸入以產生第二辨識結果。在其他實例中，可提示使用者重複第一語音輸入之至少一部分。可自使用者輸入接收為第一語音輸入之重複的第三語音輸入。可使用第二ASR系統處理第三語音輸入以產生第三辨識結果。在又其他實例中，可使用第二ASR系統處理第一語音輸入以產生第四辨識結果。在一些實例中，第二ASR系統可比第一ASR系統更精確。因此，第二辨識結果、第三辨識結果及第四辨識結果可各自比第一辨識結果更精確。
此外，在一些實例中，可藉由使用第一辨識結果及使用第二ASR系統產生之辨識結果(例如，第二辨識結果、第三辨識結果或第四辨識結果)執行ASR系統組合來判定組合結果。組合結果可比第一辨識結果更精確。
與更精確之第二ASR體系及與執行ASR系統組合相關聯之較長延時及計算時間可為減少類似錯誤重現之機率的可接受折衷。具體而言，在經歷與第一辨識結果相關聯之錯誤後，使用者可偏好等待較久以獲得後續正確結果而不是在較短時間段內獲得相同結果。本文所揭 示之系統及處理程序因此可經實施以減少在語音處理期間之類似錯誤重現之機率，從而改善使用者體驗。
1. 系統及環境
圖1說明根據各種實例之系統100的方塊圖。在一些實例中，系統100可實施數位助理。術語「數位助理」、「虛擬助理」、「智慧型自動化助理」或「自動數位助理」可係指解譯呈口頭及/或文字形式之自然語言輸入以推斷使用者意圖並基於所推斷之使用者意圖執行動作的任何資訊處理系統。舉例而言，為按推斷之使用者意圖作用，系統可執行以下各者中之一或多者：識別具有經設計以實現所推斷使用者意圖之步驟及參數的任務流程；輸入來自所推斷使用者意圖之具體需求至任務流程中；藉由調用程式、方法、服務、API或其類似者執行任務流程；及以聽覺(例如，語音)及/或視覺形式產生至使用者的輸出回應。
具體而言，數位助理可能夠接受至少部分地呈自然語言命令、請求、陳述、敍述及/或查詢形式之使用者請求。通常，使用者請求可藉由數位助理尋求任務之資訊性回答或執行。對使用者請求之良好回應可為所請求資訊性回答之提供、所請求任務之執行，或兩者之組合。舉例而言，使用者可詢問數位助理問題，諸如「我此刻在哪？」基於使用者之當前位置，數位助理可回答「你在中央公園西門附近。」使用者亦可請求任務之執行，例如「請邀請我的朋友在下一週參加我女朋友的生日聚會。」作為回應，數位助理可藉由說「是的，立刻」對請求作出應答，且接著將代表使用者之合適的行事曆邀請發送至使用者之電子通訊錄中所列出的使用者朋友中的每一者。在所請求任務之執行期間，數位助理有時可在擴展時間段內在涉及資訊之多個交換的連續對話中與使用者互動。存在與數位助理互動以請求各種任務之資訊或執行的眾多其他方式。除提供口頭回應及採用程式化動 作以外，數位助理亦可以其他視覺或音訊形式(例如，如文字、警示、音樂、視訊、動畫等)提供回應。
數位助理之實例描述於申請人之2011年1月10日申請之美國實用新型申請案第12/987,982號「Intelligent Automated Assistant」中，該申請案之整個揭示內容以引用之方式併入本文中。
如圖1中所示，在一些實例中，數位助理可根據用戶端伺服器模型實施。數位助理可包括在使用者器件104a、104b上執行之用戶端側部分102a、102b(下文稱為「DA用戶端102」及在伺服器系統108上執行之伺服器側部分106(下文稱為「DA伺服器106」。DA用戶端102可經由一或多個網路110與DA伺服器106通信。DA用戶端102可向DA伺服器106提供用戶端側功能性，諸如面向使用者之輸入及輸出處理與通信。DA伺服器106可提供用於任何數目個DA用戶端102之伺服器側功能性，該等DA用戶端各自駐留於各別使用者器件104上。
在一些實例中，DA伺服器106可包括面向用戶端之I/O介面112、一或多個處理模組114、資料及模型116，及至外部服務之I/O介面118。面向用戶端之I/O介面可促進數位助理伺服器106之面向用戶端之輸入及輸出處理。一或多個處理模組114可利用資料及模型116來處理語音輸入並基於自然語言輸入判定使用者之意圖。此外，一或多個處理模組114基於經推斷之使用者意圖進行任務執行。在一些實例中，DA伺服器106可經由網路110與外部服務120通信以用於任務完成或資訊採集。至外部服務之I/O介面118可促進此等通信。
使用者器件104之實例可包括(但不限於)手持型電腦、個人數位助理(PDA)、平板電腦、膝上型電腦、桌上型電腦、蜂巢式電話、智慧型手機、增強型通用封包無線電服務(EGPRS)行動電話、媒體播放器、導航器件、遊戲控制台、電視、電視機上盒、遙控器、可穿戴電子器件或此等資料處理器件或其他資料處理器件中之任何兩者或更多 者之組合。參考圖2中所示之例示性使用者器件104提供關於使用者器件104之更多細節。
通信網路110之實例可包括區域網路(「LAN)」及廣域網路(「WAN」)，例如網際網路。通信網路110可使用任何已知網路協定實施，該網路協定包括各種有線或無線協定，諸如乙太網路、通用串列匯流排(USB)、FIREWIRE、全球行動通信系統(GSM)、增強型資料GSM環境(EDGE)、分碼多重存取(CDMA)、分時多重存取(TDMA)、藍芽、Wi-Fi、網際網路通訊協定語音(VoIP)、Wi-MAX，或任何其他合適的通信協定。
伺服器系統108可在一或多個獨立資料處理設備或分佈式電腦網路上實施。在一些實例中，伺服器系統108亦可採用各種虛擬器件及/或第三方服務提供商(例如，第三方雲端服務提供商)之服務以提供伺服器系統108之基礎計算資源及/或基礎架構資源。
雖然圖1中所示之數位助理可包括用戶端側部分(例如，DA用戶端102)與伺服器側部分(例如，DA伺服器106)兩者，但在一些實例中，數位助理之功能可實施為使用者器件上安裝之獨立應用程式。另外，在數位助理之用戶端與伺服器部分之間的功能性之劃分可在不同實施中變化。舉例而言，在一些實例中，DA用戶端可為僅提供面向使用者之輸入及輸出處理功能且將數位助理之所有其他功能性委派給後端伺服器之精簡性用戶端。
2. 使用者器件
圖2說明根據各種實例之使用者器件104的方塊圖。使用者器件104可包括記憶體介面202、一或多個處理器204及周邊器件介面206。使用者器件104中之各種組件可藉由一或多個通信匯流排或信號線耦接。使用者器件104可包括耦接至周邊器件介面206之各種感測器、子系統及周邊器件。感測器、子系統及周邊器件可收集資訊及/或促進 使用者器件104之各種功能性。
舉例而言，運動感測器210、光感測器212及近接感測器214可耦接至周邊器件介面206以促進定向、照明及近接感測功能。一或多個其他感測器216(諸如，定位系統(例如，GPS接收器)、溫度感測器、生物測定感測器、陀螺儀、羅盤、加速度計及類似者)亦可連接至周邊器件介面206來促進相關功能性。
在一些實例中，攝影機子系統220及光學感測器222可用以促進攝影機功能，諸如拍照及錄製視訊短片。可經由一或多個有線及/或無線通信子系統224來促進通信功能，有線及/或無線通信子系統224可包括各種通信埠、射頻接收器及傳輸器及/或光學(例如，紅外線)接收器及傳輸器。音訊子系統226可耦接至揚聲器228及麥克風230以有助於具備話音能力的功能，諸如，話音辨識、話音複寫、數位錄製及電話功能。麥克風230可經組態以接收來自使用者之語音命令。
在一些實例中，I/O子系統240亦可耦接至周邊器件介面206。I/O子系統240可包括觸控式螢幕控制器242及/或其他輸入控制器244。觸控式螢幕控制器242可耦接至觸控式螢幕246。觸控式螢幕246及觸控式螢幕控制器242可(例如)使用複數個觸敏技術(諸如，電容性、電阻性、紅外線及表面聲波技術)、近接感測器陣列及其類似者中之任一者偵測接觸及其移動或中斷。其他輸入控制器244可耦接至其他輸入/控制器件248，諸如一或多個按鈕、搖臂開關、拇指旋輪、紅外線埠、USB埠及/或諸如觸控筆之指標器件。
在一些實例中，記憶體介面202可耦接至記憶體250。記憶體250可包括任何電子、磁性、光學、電磁、紅外線或半導體系統、裝置或器件；攜帶型電腦磁片(磁性)；隨機存取記憶體(RAM)(磁性)；唯讀記憶體(ROM)(磁性)；可抹除可程式化唯讀記憶體(EPROM)(磁性)；諸如CD、CD-R、CD-RW、DVD、DVD-R或DVD-RW之攜帶型光碟； 或諸如緊密快閃卡、安全數位卡、USB記憶體器件、記憶棒及其類似者之快閃記憶體。在一些實例中，記憶體250之非暫時性電腦可讀儲存媒體可用以儲存指令(例如，用於執行以下描述之處理程序400)以用於供指令執行系統、裝置或器件(諸如，基於電腦之系統、含處理器之系統或可自指令執行系統、裝置或器件提取指令且執行該等指令的其他系統)使用或結合指令執行系統、裝置或器件使用。在其他實例中，指令(例如，用於執行以下描述之處理程序400)可儲存在伺服器系統108之非暫時性電腦可讀儲存媒體(圖中未展示)上，或可在記憶體250之非暫時性電腦可讀儲存媒體與伺服器系統108之非暫時性電腦可讀儲存媒體之間劃分。在本文獻之上下文中，「非暫時性電腦可讀儲存媒體」可為可含有或儲存用於供指令執行系統、裝置或器件使用或結合指令執行系統、裝置或器件使用之程式的任何媒體。
在一些實例中，記憶體250可儲存作業系統252、通信模組254、使用者介面模組256、感測器處理模組258、電話模組260及應用程式262。作業系統252可包括用於處置基本系統服務及用於執行硬體相依任務之指令。通信模組254可促進與一或多個額外器件、一或多個電腦及/或一或多個伺服器通信。使用者介面模組256可使用其他輸出頻道(例如，揚聲器)來促進圖形使用者介面處理及輸出處理。感測器處理模組258可促進感測器相關處理及功能。電話模組260可促進電話相關處理程序及功能。應用程式模組262可促進使用者應用程式之各種功能性，諸如電子訊息傳遞、網站瀏覽、媒體處理、導覽、成像及/或其他處理程序及功能。
如本文所描述，記憶體250亦可儲存用戶端側數位助理指令(例如，在數位助理用戶端模組264中)及各種使用者資料266(例如，使用者特定詞彙資料、偏好資料及/或其他資料，諸如使用者之電子通訊錄、待辦清單、購物清單、使用者指定之名稱發音等)，以提供數位 助理之用戶端側功能性。
在各種實例中，數位助理用戶端模組264可能能夠經由使用者器件104之各種使用者介面(例如，I/O子系統244)接受話音輸入(例如，語音輸入)、文字輸入、觸摸輸入及/或示意動作輸入。數位助理用戶端模組264亦可能能夠以音訊(例如，語音輸出)、視覺及/或觸覺形式提供輸出。舉例而言，輸出可經提供為話音、聲音、警示、文字訊息、功能表、圖形、視訊、動畫、振動及/或上述中兩者或更多者之組合。在操作期間，數位助理用戶端模組264可使用通信子系統224與數位助理伺服器106通信。
在一些實例中，數位助理用戶端模組264可利用各種感測器、子系統及周邊器件來收集來自使用者器件104之周圍環境之額外資訊以建立與使用者、當前使用者互動及/或當前使用者輸入相關聯之上下文。在一些實例中，數位助理用戶端模組264可將具有使用者輸入之上下文資訊或其子集提供至數位助理伺服器以有助於推斷使用者之意圖。在一些實例中，數位助理亦可使用上下文資訊來判定如何準備及遞送輸出至使用者。
在一些實例中，伴隨使用者輸入之上下文資訊可包括感測器資訊，例如照明、環境雜訊、環境溫度、周圍環境之影像或視訊等。在一些實例中，上下文資訊亦可包括器件之實體狀態，例如，器件定向、器件位置、器件溫度、功率位準、速度、加速度、運動模式、蜂巢式信號強度等。在一些實例中，可將與使用者器件104之軟體狀態有關之資訊(例如，使用者器件104之執行中處理程序、安裝之程式、過去與現在網路活動、背景服務、錯誤日誌、資源利用等)提供至數位助理伺服器作為與使用者輸入相關聯之上下文資訊。
在一些實例中，回應於來自數位助理伺服器之請求，DA用戶端模組264可選擇性地提供儲存於使用者器件104上之資訊(例如，使用 者資料266)。在一些實例中，數位助理用戶端模組264亦可根據數位助理伺服器106之請求經由自然語言對話或其他使用者介面引出來自使用者之額外輸入。數位助理用戶端模組264可將額外輸入傳遞至數位助理伺服器106以有助於數位助理伺服器106對在使用者請求中表達的使用者意圖進行意圖推斷及/或履行。
在各種實例中，記憶體250可包括額外指令或較少指令。舉例而言，DA用戶端模組264可包括圖3A中所描述之數位助理模組326之子模組中之任一者。此外，使用者器件104之各種功能可實施於硬體中及/或韌體中，包括實施於一或多個信號處理及/或特殊應用積體電路中。
3. 數位助理系統
圖3A說明根據各種實例之實例數位助理系統300之方塊圖。在一些實例中，數位助理系統300可實施於獨立電腦系統上。在一些實例中，數位助理系統300可跨越多個電腦分佈。在一些實例中，數位助理之一些模組及功能可經劃分為伺服器部分及用戶端部分，其中用戶端部分駐留於使用者器件(例如，使用者器件104)上且經由一或多個網路與伺服器部分(例如，伺服器系統108)通信，例如，如圖1中所示。在一些實例中，數位助理系統300可為圖1中所示之伺服器系統108(及/或數位助理伺服器106)之實施。應注意，數位助理系統300僅為數位助理系統之一項實例，且數位助理系統300可具有比所示更多或更少之組件，可組合兩個或兩個以上組件，或可具有不同的組件組態或配置。圖3A中所示之各種組件可以硬體、用於由一或多個處理器執行之軟體指令、韌體來實施，其包括一或多個信號處理及/或特殊應用積體電路或其組合。
數位助理系統300可包括記憶體302、一或多個處理器304、輸入/輸出(I/O)介面306及網路通信介面308。此等組件可經由一或多個通信 匯流排或信號線310彼此通信。
在一些實例中，記憶體302可包括非暫時性電腦可讀媒體，諸如，高速隨機存取記憶體及/或非揮發性電腦可讀儲存媒體(例如，一或多個磁碟儲存器件、快閃記憶體器件或其他非揮發性固態記憶體器件)。
在一些實例中，I/O介面306可將數位助理系統300之輸入/輸出器件316(諸如，顯示器、鍵盤、觸控式螢幕及麥克風)耦接至使用者介面模組322。I/O介面306結合使用者介面模組322可接收使用者輸入(例如，話音輸入、鍵盤輸入、觸摸輸入等)且相應地處理該等使用者輸入。在一些實例中，例如，當數位助理實施於獨立使用者器件上時，數位助理系統300可包括關於圖2中之使用者器件104描述之組件與I/O及通信介面中之任一者。在一些實例中，數位助理系統300可表示數位助理實施之伺服器部分，且可經由駐留於使用者器件(例如，圖2中所示之使用者器件104)上之用戶端側部分與使用者互動。
在一些實例中，網路通信介面308可包括有線通信埠312及/或無線傳輸及接收電路314。有線通信埠可經由一或多個有線介面(例如，乙太網路、通用串列匯流排(USB)、FIREWIRE等)接收及發送通信信號。無線電路314可自通信網路及其他通信器件接收RF信號及/或光學信號/將該等信號發送至通信網路及其他通信器件。無線通信可使用複數個通信標準、協定及技術(諸如，GSM、EDGE、CDMA TDMA、藍芽、Wi-Fi、VoIP、Wi-MAX或任何其他合適的通信協定)中之任一者。網路通信介面308可致能數位助理系統300與諸如網際網路、企業內部網路及/或無線網路(諸如蜂巢式電話網路、無線區域網路(LAN)及/或都會區域網路(MAN))之網路及其他器件之間的通信。
在一些實例中，記憶體302或記憶體302之電腦可讀儲存媒體可儲存程式、模組、指令及包括以下中之所有或子集之資料結構：作業 系統318、通信模組320、使用者介面模組322、一或多個應用程式324及數位助理模組326。詳言之，記憶體302或記憶體302之電腦可讀儲存媒體可儲存用於執行下文所描述之處理程序400之指令。一或多個處理器304可執行此等程式、模組及指令，且自資料結構進行讀取/寫入至資料結構。
作業系統318(例如，Darwin、RTXC、LINUX、UNIX、OS X、WINDOWS或諸如VxWorks之嵌入式作業系統)可包括用於控制及管理通用系統任務(例如，記憶體管理、儲存器件控制、功率管理等)之各種軟體組件及/或驅動程式，且促進各種硬體、韌體及軟體組件之間的通信。
通信模組320可促進在數位助理系統300與其他器件之間經由網路通信介面308進行之通信。舉例而言，通信模組320可與圖2中所示之器件104之通信模組254通信。通信模組320亦可包括用於處置藉由無線電路314及/或有線通信埠312接收之資料的各種組件。
使用者介面模組322可經由I/O介面306接收來自使用者之命令及/或輸入(例如，來自鍵盤、觸控式螢幕、指標器件、控制器及/或麥克風)，且在顯示器上產生使用者介面物件。使用者介面模組322亦可準備輸出(例如，語音、聲音、動畫、文字、圖示、振動、觸感回饋、光等)且將該等輸出經由I/O介面306(例如，經由顯示器、音訊頻道、揚聲器、觸控板等)遞送至使用者。
應用程式324可包括經組態以藉由一或多個處理器304執行之程式及/或模組。舉例而言，若數位助理系統實施於獨立使用者器件上，則應用程式324可包括使用者應用程式，諸如，遊戲、行事曆應用程式、導航應用程式或電子郵件應用程式。若數位助理系統300實施於伺服器群上，則應用程式324可包括(例如)資源管理應用程式、診斷應用程式或排程應用程式。
記憶體302亦可儲存數位助理模組(或數位助理之伺服器部分)326。在一些實例中，數位助理模組326可包括以下子模組或其子集或超集：輸入/輸出處理模組328、語音轉文字(STT)處理模組330、自然語言處理模組332、對話流程處理模組334、任務流程處理模組336及服務處理模組338。此等模組中之每一者可存取數位助理326之以下資料及模型或其子集或超集中的一或多者：本體360、詞彙索引344、使用者資料348、任務流程模型354及服務模型356。
在一些實例中，使用實施於數位助理模組326中之處理模組、資料及模型，數位助理可執行以下各者中之至少一些：將語音輸入轉換成文字；識別以自使用者接收之自然語言輸入表達的使用者意圖；主動地引發及獲得所需要的資訊以充分推斷使用者意圖(例如，藉由消除字、遊戲、意圖等的歧義)；判定用於實現經推斷意圖之任務流程；及執行任務流程以實現經推斷意圖。
在一些實例中，如圖3B中所示，I/O處理模組328可經由圖3A中之I/O器件316與使用者互動或經由圖3A中之網路通信介面308與使用者器件(例如，圖1中之使用者器件104)互動，以獲得使用者輸入(例如，語音輸入)及提供對使用者輸入之回應(例如，作為語音輸出)。I/O處理模組328可視情況連同使用者輸入之接收一起或在其不久後獲得與來自使用者器件之使用者輸入相關聯之上下文資訊。上下文資訊可包括使用者特定資料、詞彙及/或體與使用者輸入相關之偏好。在一些實例中，上下文資訊亦包括在接收使用者請求時之器件(例如，圖1中之使用者器件104)之軟體硬體狀態，及/或在接收使用者請求時與使用者之周圍環境相關之資訊。在一些實例中，I/O處理模組328亦可發送關於使用者請求之後續問題至使用者且接收來自使用者之回答。在由I/O處理模組328接收使用者請求且使用者請求可包括語音輸入時，I/O處理模組328可將語音輸入轉寄至STT處理模組330(或語音 辨識器)以用於語音轉文字轉換。
STT處理模組330可包括一或多個ASR系統。一或多個ASR系統可處理經由I/O處理模組328接收之語音輸入以產生辨識結果。每一ASR系統可包括前端語音預處理器。前端語音預處理器可自語音輸入擷取代表性特徵。舉例而言，前端語音預處理器可對語音輸入執行傅里葉變換以擷取將語音輸入表徵為代表性多維向量之序列之光譜特徵。此外，每一ASR系統可包括一或多個語音辨識模型(例如，聲波模型及/或語言模型)且可實施一或多個語音辨識引擎。語音辨識模型之實例可包括隱式馬爾可夫模型、高斯混合模型、深層神經網路模型、n元語法模型及其他統計模型。語音辨識引擎之實例可包括基於動態時間規整之引擎及基於加權有限狀態換能器(WFST)之引擎。一或多個語音辨識模型及一或多個語音辨識引擎可用以處理前端語音預處理器之擷取之代表性特徵，以產生中間辨識結果(例如，音素、音素序列及子字詞)，且最終產生文字辨識結果(例如，字詞、字詞序列或語彙基元(Token)序列)。在一些實例中，語音輸入可至少部分地由第三方服務處理或在使用者之器件(例如，使用者器件104)上處理以產生辨識結果。一旦STT處理模組330產生含有文字辨識結果(例如，字詞、字詞序列或語彙基元序列)，則可將辨識結果傳遞至自然語言處理模組332以用於意圖推論。
在一些實例中，STT處理模組330可包括及/或經由語音字母轉換模組331存取可辨識詞之詞彙。每一詞彙字可與表示於語音辨識語音字母中之字的一或多個候選發音相關聯。舉例而言，詞彙可包括與「tuh-may-doe」及「tuh-mah-doe」之候選發音關聯之字語「番茄」。在一些實例中，可基於字詞之拼寫及一或多個語言及/或語音規則判定字詞之候選發音。在一些實例中，可例如基於已知典型發音手動產生候選發音。
在一些實例中，可基於候選發音之共同性對候選發音進行排名。舉例而言，候選發音「tuh-may-doe」之排名可比「tuh-mah-doe」高，此係因為前者係更常用之發音(例如，在所有使用者中，用於處於特定地理區域中之使用者，或用於使用者之任何其他適當子集)。在一些實例中，候選發音中之一者可經選擇為經預測發音(例如，最可能發音)。
當接收到語音輸入時，STT處理模組330可用以判定對應於語音輸入之音素(例如，使用聲波模型)且接著試圖判定匹配音素之字詞(例如，使用語言模型)。舉例而言，若STT處理模組330可首先識別對應於語音輸入之一部分之音素序列「tuh-may-doe」，則其可接著基於詞彙索引344判定此序列對應於字詞「番茄」。
在一些實例中，STT處理模組330可使用近似匹配技術以判定話語中之字詞。因此，例如，STT處理模組330可判定音素序列「duh-may-doe」對應於字詞「番茄」，即使特定音素序列並非為該字詞之候選音素中之一者。
在一些實例中，STT處理模組330可能夠基於兩個或兩個以上辨識結果判定組合結果。舉例而言，STT處理模組330可能夠執行下文所描述之處理程序400之區塊414、424及430。
數位助理之自然語言處理模組332(「自然語言處理器」)可獲取由STT處理模組330產生之字詞或語彙基元之序列(「詞彙基元序列」)且試圖將詞彙基元序列與由數位助理辨識之一或多個「可動作意圖」相關聯。「可動作意圖」可表示可由數位助理執行之任務，且可具有實施於任務流程模型354中之相關聯任務流程。相關聯任務流程可為數位助理採用以執行任務之一系列程式化動作及步驟。數位助理之能力之範疇可取決於已實施及儲存於任務流程模型354中之大量及多種任務流程，或亦即，取決於數位助理辨識之大量及多種「可動作意 圖」。然而，數位助理之有效性亦可取決於該助理自以自然語言表達之使用者請求推斷正確「可動作意圖」的能力。
在一些實例中，除自STT處理模組330獲得之字詞或詞彙基元序列之外，自然語言處理模組332亦可例如自I/O處理模組328接收與使用者請求相關聯之上下文資訊。自然語言處理模組332可視情況使用上下文資訊以澄清、補充及/或進一步定義自STT處理模組330接收之詞彙基元序列中所含之資訊。上下文資訊可包括(例如)使用者偏好、使用者器件之硬體及/或軟體狀態、在使用者請求之前、期間或不久後收集之感測器資訊、數位助理與使用者之間的先前互動(例如，對話)及其類似者。如本文所描述，上下文資訊可為動態的，且可隨對話之時間、位置、內容及其他因素變化。
在一些實例中，自然語言處理可基於(例如)本體360。本體360可為含有許多節點之階層式結構，每一節點表示與「可動作意圖」或其他「屬性」中之一或多者相關之「可動作意圖」或「屬性」。如上所述，「可動作意圖」可表示數位助理能夠執行之任務，亦即，其為「可動作的」或可付諸行動。「屬性」可表示與可動作意圖或另一屬性之子態樣相關聯之參數。本體360中之可動作意圖節點與屬性節點之間的連結可定義由屬性節點表示之參數如何係關於由可動作意圖節點表示之任務。
自然語言處理模組332可自STT處理模組330接收詞彙基元序列(例如，文字字串)且判定哪些節點由詞彙基元序列中之字詞暗示。在一些實例中，若(經由詞彙索引344)發現詞彙基元序列中之字詞或片語與本體360中之一或多個節點相關聯，則字詞或片語可「觸發」或「激活」彼等節點。基於經激活節點之數量及/或相對重要性，自然語言處理模組332可選擇可動作意圖中之一者作為使用者意欲數位助理執行之任務。在一些實例中，可選擇具有大部分「經觸發」節點之 網域。在一些實例中，可選擇具有最高信賴值之網域(例如，基於其各種經觸發節點之相對重要性)。在一些實例中，可基於經觸發節點之數目及重要性之組合選擇網域。在一些實例中，亦在選擇節點中考慮額外因素，諸如，數位助理先前是否已正確解譯來自使用者之類似請求。
使用者資料348可包括使用者特定之資訊，諸如，使用者特定之詞彙、使用者偏好、使用者位址、使用者之預設及次要語言、使用者之連絡人清單及針對每一使用者之其他長期或短期資訊。在一些實例中，自然語言處理模組332可使用使用者特定之資訊來補充使用者輸入中所含之資訊以進一步定義使用者意圖。舉例而言，對於使用者請求「邀請我的朋友參加我的生日聚會」，自然語言處理模組332可能能夠存取使用者資料348以判定「朋友」是誰及「生日聚會」將於何時及何地舉辦，而不是要求使用者在其請求中明確提供此類資訊。
在一些實例中，一旦自然語言處理模組332基於使用者請求識別可動作意圖(或網域)，則自然語言處理模組332可產生結構化查詢以表示經識別之可動作意圖。在一些實例中，結構化查詢可包括用於可動作意圖之網域內之一或多個節點之參數，且至少一些該等參數填充有在使用者請求中指定之特定資訊及要求。舉例而言，使用者可說「為我在吃壽司的地方預訂7點的晚餐。」在此情況下，自然語言處理模組332可能能夠基於使用者輸入將可動作意圖正確地識別為「餐廳預訂」。根據本體，對於「餐廳預訂」網域之結構化查詢可包括諸如{菜式}、{時間}、{日期}、{派對大小}及其類似者之參數。在一些實例中，基於語音輸入及使用STT處理模組330自該語音輸入導出之文字，自然語言處理模組332可產生對於餐廳預訂網域之部分結構化查詢，其中該部分結構化查詢包括參數{菜式=「壽司」}及{時間=「7pm」}。然而，在此實例中，使用者之話語含有不足以完成與網 域相關聯之結構化查詢之資訊。因此，可並未基於當前可用之資訊在結構化查詢中指定諸如{派對大小}及{日期}之其他必需參數。在一些實例中，自然語言處理模組332可向結構化查詢之一些參數填充已接收之上下文資訊。舉例而言，在一些實例中，若使用者請求「我附近的」壽司餐廳，則自然語言處理模組332可向結構化查詢中之{位置}參數填充來自使用者器件104之GPS座標。
在一些實例中，自然語言處理模組332可將結構化查詢(包括任何完整參數)傳遞至任務流程處理模組336(「任務流程處理器」)。任務流程處理模組336可經組態以自自然語言處理模組332接收結構化查詢，在必要時完成結構化查詢且執行所需動作以「完成」使用者之最終請求。在一些實例中，可在任務流程模型354中提供完成此等任務所需之各種程序。在一些實例中，任務流程模型可包括用於自使用者獲得額外資訊之程序及用於執行與可動作意圖相關聯之動作的任務流程。
如上文所描述，為了完成結構化查詢，任務流程處理模組336可需要起始與使用者之額外對話以便獲得額外資訊，及/或消除潛在不明確話語之歧義。當此類互動係必需的時，任務流程處理模組336可調用對話流程處理模組334以參加與使用者之對話。在一些實例中，對話流程處理模組334可判定如何(及/或何時)向使用者請求額外資訊，且接收及處理使用者回應。可經由I/O處理模組328向使用者提供問題且可接收來自使用者之回答。在一些實例中，對話流程處理模組334可經由音訊及/或視覺輸出向使用者呈現對話輸出且經由口頭或實體(例如，點選)回應接收來自使用者之輸入。繼續以上實例，當任務流程處理模組336調用對話流程處理模組334來針對與網域「餐廳預訂」相關聯之結構化查詢判定「派對大小」及「日期」資訊時，對話流程處理模組334可產生諸如「供多少人？」及「在哪一天？」之問 題以傳遞至使用者。一旦接收來自使用者之到回答，對話流程處理模組334可接著向結構化查詢填充遺失資訊，或將該資訊傳遞至任務流程處理模組336以使來自結構化查詢之遺失資訊完整。
一旦任務流程處理模組336已完成對於可動作意圖之結構化查詢，則任務流程處理模組336可繼續執行與可動作意圖相關聯之最終任務。因此，任務流程處理模組336可根據結構化查詢中所含之特定參數來執行任務流程模型中之步驟及指令。舉例而言，用於「餐廳預訂」之可動作意圖之任務流程模型可包括用於連絡餐廳及實際上請求在特定時間用於特定派對大小之預訂的步驟及指令。舉例而言，使用結構化查詢，諸如：{餐廳預訂，餐廳=ABC咖啡館，日期=3/12/2012，時間=7pm，派對大小=5}，任務流程處理模組336可執行以下步驟：(1)登入至ABC咖啡館或餐廳預訂系統之伺服器(諸如，OPENTABLE®)上；(2)在網站上之表格中鍵入日期、時間及派對大小資訊；(3)提交表格及(4)在使用者之行事曆中作出針對預訂之行事曆條目。
在一些實例中，任務流程處理模組336可採用服務處理模組338(「服務處理模組」)之輔助以完成使用者輸入中所請求之任務或提供使用者輸入中所請求之資訊性回答。舉例而言，服務處理模組338可代表任務流程處理模組336作用以進行通話、設定行事曆條目、調用地圖搜尋、調用安裝於使用者器件上之其他使用者應用程式或與其互動，及調用第三方服務(例如，餐廳預訂入口網站、社交網路連接網站、銀行入口網站等)或與其互動。在一些實例中，每一服務所需之協定及應用程式設計介面(API)可藉由服務模型356中之各別服務模型指定。服務處理模組338可針對服務存取適當服務模型且根據服務所需之協定及API根據服務模型產生對於服務之請求。
舉例而言，若餐廳已啟用連線預訂服務，則餐廳可向連線預訂 服務提交服務模型，該服務模型指定用於進行預訂之必要參數及用於傳達必要參數之值之API。當由任務流程處理模組336請求時，服務處理模組338可使用儲存於服務模型上之網站位址建立與連線預訂服務之網路連接，且根據連線預訂服務之API以一格式將預訂之必要參數(例如，時間、日期、派對大小)發送至連線預訂介面。
在一些實例中，可共同地及反覆地使用自然語言處理模組332、對話流程處理模組334及任務流程處理模組336來推斷及定義使用者之意圖，獲得用以進一步澄清及精簡使用者意圖之資訊，且最終產生回應(亦即，至使用者之輸入或任務之完成)以實現使用者之意圖。
可在2010年1月18日申請之名為「Intelligent Automated Assistant」之美國實用新型申請案第12/987,982號以及2011年6月3日申請之名為「Generating and Processing Data Items That Represent Tasks to Perform」之美國實用新型申請案第61/493,201號中找到關於數位助理之額外細節，以上申請案之全部揭示內容以引用之方式併入本文中。
4. 用於數位助理中之語音處理之處理程序
圖4A至圖4B說明根據各種實例之用於處理語音之處理程序400。處理程序400可在電子器件處執行，該電子器件具有一或多個處理器及儲存供該一或多個處理器執行之一或多個程式之記憶體。在一些實例中，處理程序400可在使用者器件104或伺服器系統108處執行。在一些實例中，處理程序400可由數位助理系統300(圖3A)執行，如上所述，該數位助理系統可實施於獨立電腦系統(例如，使用者器件104抑或伺服器系統108)中或跨越多個電腦(例如，使用者器件104、伺服器系統108及/或額外或替代器件或系統)分佈。雖然以下論述將處理程序400描述為由數位助理(例如，數位助理系統300)執行，但該處理程序並不限於藉由任何特定器件、器件之組合或實施之執行。此外， 處理程序之獨立區塊可以任何適當方式跨越一或多個電腦、系統或器件分佈。
在處理程序400之區塊402處，可接收來自使用者之第一語音輸入。在一些實例中，可在與數位助理之互動過程中或作為該互動之一部分接收第一語音輸入。在其他實例中，第一語音輸入可為待由數位助理轉錄以用於輸入至電子器件之應用程式(例如，電子郵件、文書處理、訊息、網站搜尋及其類似者)中之聽寫。可接收呈聲波、音訊文件或代表性音訊信號(模擬或數位)形式之第一語音輸入。在一些實例中，第一語音輸入可為可藉由電子器件(例如，使用者器件104)之麥克風(例如，麥克風230)接收之聲波。在其他實例中，第一語音輸入可為藉由電子器件之音訊子系統(例如，音訊子系統226)、周邊介面(例如，周邊介面206)或處理器(例如，處理器204)接收之代表性音訊信號或錄製之音訊文件。在又其他實例中，第一語音輸入可為藉由數位助理系統之I/O介面(例如，I/O介面306)或處理器(例如，處理器304)接收之代表性音訊信號或錄製之音訊文件。
在一些實例中，第一語音輸入可包括使用者請求。使用者請求可為任何請求，包括指示數位助理可執行之任務(例如，進行及/或便於餐廳預訂，起始電話通話或文字訊息)的請求、對於回應(例如，對諸如「地球離太遠多遠」之問題之回答)之請求及類似者。
在處理程序400之區塊404處，可使用第一ASR系統處理第一語音輸入以產生第一辨識結果。習知語音轉文字處理技術可用於處理第一語音輸入。在一些實例中，可使用電子器件之STT處理模組(例如，STT處理模組330)處理第一語音輸入以產生第一辨識結果。具體而言，STT處理模組可包括第一ASR系統。第一ASR系統之一或多個語音辨識模型(例如，聲波模型及/或語言模型)及第一ASR系統之一或多個語音辨識引擎可用於處理第一語音輸入。
在一些實例中，第一辨識結果可包括中間辨識結果，諸如音素及子字詞(例如，音節、語素及類似者)。詳言之，第一辨識結果可包括對應於第一語音輸入之音素或語彙基元序列。對應於第一語音輸入之此音素序列可被稱作第一語音輸入之語音轉錄。在一些實例中，第一辨識結果可包括諸如對應於第一語音輸入之至少一部分之字詞或字詞序列的文字。此外，在一些實例中，處理第一語音輸入可包括判定用於自第一語音輸入導出之每一音素、音素序列、子字詞、字詞或字詞序列之信賴度量。信賴度量可反映辨識結果之信賴度或精確性。在一些實例中，第一辨識結果可因此包括所導出之音素、音素序列、子字詞、字詞或字詞序列之信賴度量。
在處理程序400之區塊406處，可執行基於第一辨識結果之動作。在其中處理程序400經實施以轉錄語音以便輸入至電子器件之應用程式(例如，電子郵件、訊息傳遞、文書處理、網頁搜尋及類似者)之實例中，該辨識結果可包括自第一語音輸入之至少一部分轉錄的文字。在此等實例中，動作可包括在電子器件上(例如，在觸控式螢幕246上)顯示第一辨識結果之文字之至少一部分。
在其他實例中，第一語音輸入可包括針對數位助理之使用者請求。在此等實例中，動作可包括執行滿足使用者請求之任務。具體而言，動作可包括執行意欲實現(例如，使用自然語言處理模組332、對話流程處理模組334及任務流程處理模組336判定的)與使用者請求相關聯之使用者之意圖的任務。舉例而言，第一語音輸入可包括使用者請求「撥接Tim Carpenter。」第一辨識結果可因此包括文字「撥接Tim Carpenter。」基於此文字，數位助理可執行撥接與使用者之連絡人清單中列出之個人「Tim Carpenter」相關聯之電話號碼之任務。
在又其他實例中，動作可包括產生概述、描述或確定藉由數位助理自第一文字(例如，使用自然語言處理模組332)推斷之意圖的輸 出(例如，語音或文字)。在一項此類實例中，數位助理可基於含有文字「針對松樹之網頁搜尋。」之第一辨識結果輸出片語「針對關於松樹之資訊搜尋網頁。」在另一此類實例中，數位助理可基於含有文字r撥接Tim Carpenter。」之第一辨識結果輸出問題「你希望撥接Tim Carpenter嗎？」
在處理程序400之區塊408處，可接收來自使用者之指示第一辨識結果中之潛在錯誤之輸入。在一些實例中，潛在錯誤可由使用者基於在區塊406處執行之動作推斷。在一項此類實例中，在區塊402處自使用者接收之第一語音輸入可包括「請撥接Tim Carpenter。」然而，在區塊406處藉由數位助理執行之動作可包括撥接「Jim Carpenter。」在此實例中，使用者可推斷第一辨識結果中存在潛在錯誤且因此可在電子器件處接收來自使用者之指示潛在錯誤之輸入。
在區塊408處接收之輸入可為指示第一辨識結果中之潛在錯誤之任何輸入。在一些實例中，該輸入可包括第二語音輸入。在此等實例中，可在區塊410處處理第二語音輸入以判定第二語音輸入是否為區塊402之第一語音輸入之至少一部分的重複。使用者對第一語音輸入之至少一部分的重複可指示第一辨識結果中之潛在錯誤。
在處理程序400之區塊410處，可判定區塊408處之輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入。判定輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入可係合乎需要的，以判定在區塊408處接收之輸入是否可用於改善辨識結果及減小相同錯誤重現之機率。舉例而言，若判定輸入包括為第一語音輸入之至少一部分之重複的第二語音輸入，則第二語音輸入可經處理以產生可用於改善辨識結果之第二辨識結果(例如，在區塊412處)。
在一些實例中，判定可包括比較第二語音輸入之音訊波形與第一語音輸入之音訊波形。在此等實例中，若判定第二語音輸入之音訊 波形實質上類似於第一語音輸入之音訊波形之對應部分，則第二語音輸入可經判定為第一語音輸入之至少一部分之重複。詳言之，若第二語音輸入之音訊波形與第一語音輸入之音訊波形之對應部分之間的差異小於預定臨限值，則第二語音輸入可經判定為第一語音輸入之至少一部分之重複。
在其他實例中，判定第二語音輸入是否為第一語音輸入之至少一部分之重複可包括比較第二語音輸入之語音轉錄與第一語音輸入之語音轉錄。在此等實例中，最初可使用ASR系統(例如，區塊404處之第一ASR系統，區塊412處之第二ASR系統及其類似者)處理第二語音輸入以產生第二語音輸入之語音轉錄。接著可判定第二語音輸入之語音轉錄相對於第一語音輸入之對應部分之語音轉錄的錯誤率。亦即，可將第二語音輸入之語音轉錄與第一語音輸入之對應部分之語音轉錄進行比較以判定錯誤率。在此等實例中，若第二語音輸入之語音轉錄相對於第一語音輸入之對應部分之語音轉錄的錯誤率小於預定值，則第二語音輸入可經判定為第一語音輸入之至少一部分之重複。
應認識到，第二語音輸入可為在區塊408處接收之輸入之僅一部分。舉例而言，第一語音輸入可包括「撥接Tim Carpenter」且在區塊408處接收之輸入可包括語音「不，不是Jim，我說的是『Tim Carpenter』。」在此實例中，在區塊408處接收之輸入包括第二語音輸入「Tim Carpenter」，其為第一語音輸入之至少一部分之重複。此外，應認識到，區塊410可包括判定區塊408處之輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入所必需的其他步驟。舉例而言，區塊410可另外包括判定在區塊408處接收之輸入是否包括語音。
在處理程序400之區塊412處，可使用第二ASR系統處理第二語音輸入以產生第二辨識結果。使用第二ASR系統處理第二語音輸入可類 似於在區塊404處使用第一ASR系統處理第一語音。在一些實例中，可使用電子器件之STT處理模組(例如，STT處理模組330)處理第二語音輸入以產生第二辨識結果。具體而言，STT處理模組可包括第二ASR系統。第二ASR系統之一或多個語音辨識模型(例如，聲波模型及/或語言模型)及第二ASR系統之一或多個語音辨識引擎可用於處理第二語音輸入。
在一些實例中，第二辨識結果可包括中間辨識結果，諸如音素及子字詞(例如，音節、語素及類似者)。詳言之，第二辨識結果可包括對應於第二語音輸入之音素或語彙基元序列。對應於第二語音輸入之此音素序列可被稱作第二語音輸入之語音轉錄。在一些實例中，第二辨識結果可包括諸如對應於第二語音輸入之至少一部分之字詞或字詞序列的文字。此外，在一些實例中，處理第二語音輸入可包括判定用於自第二語音輸入導出之每一音素、音素序列、子字詞、字詞或字詞序列之信賴度量。信賴度量可反映辨識結果之信賴度或精確性。在一些實例中，第二辨識結果可因此包括所導出之音素、音素序列、子字詞、字詞或字詞序列之信賴度量。
在一些實例中，第二ASR系統可為與第一ASR系統相同之ASR系統。因此，在此等實例中，可使用相同ASR系統處理在區塊402處接收之第一語音輸入及在區塊408處接收之輸入之第二語音輸入兩者。舉例而言，相較於第一語音輸入，使用者更清晰地表達第二語音輸入。此外，使用者可較大聲地說話且因此第二語音輸入可具有比第一語音輸入高的信雜比。因此，在一些實例中，儘管使用相同ASR系統，但所產生之第二辨識結果可比第一辨識結果精確。以此方式，可使用在區塊408處接收之輸入改善辨識結果且可避免相同錯誤。
在其他實例中，第二ASR系統可不同於第一ASR系統。在此等實例中，可使用不同ASR系統處理在區塊402處接收之第一語音輸入及 在區塊408處接收之輸入之第二語音輸入。使用不同ASR系統可有利於產生針對第二語音輸入之更精確的辨識結果且因此減小造成相同錯誤之機率。舉例而言，所使用之第二ASR系統之錯誤率可低於第一ASR系統之錯誤率，且因此第二ASR系統可產生比第一ASR系統精確的辨識結果。此外，第二ASR系統可能需要比第一ASR系統大的計算成本以達成較大精確性。因此，第二ASR系統之延時可大於第一ASR系統之延時。在一些實例中，第二ASR系統之一或多個語音辨識引擎可不同於第一ASR系統之一或多個語音辨識引擎。詳言之，第二ASR系統之語音辨識引擎可比第一ASR系統之語音辨識引擎穩固。在一些實例中，第二ASR系統之一或多個語音辨識模型可不同於第一ASR系統之一或多個語音辨識模型。詳言之，第二ASR系統之一或多個語音辨識模型可具有比第一ASR系統之一或多個語音辨識模型大的詞彙。因此，在一些實例中，第二辨識結果可具有比第一辨識結果高之信賴度量。
在一些實例中，可回應於在區塊410處判定輸入包括為第一語音輸入之至少一部分之重複的第二語音輸入來執行區塊412。在一項此類實例中，最初可使用預設ASR系統(例如，區塊404處之第一ASR系統)處理在區塊408處接收之輸入之第二語音輸入以產生初始辨識結果。接著可基於比較此初始辨識結果與第一辨識結果來判定輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入。回應於判定區塊408之輸入為包括第一語音輸入之至少一部分之重複的第二語音輸入，可使用更精確的ASR系統(例如，第二ASR系統)處理第二語音輸入以產生第二辨識結果。第二辨識結果可不同於初始辨識結果，其中第二辨識結果可具有比初始辨識結果高的信賴度量。
在其他實例中，可在於區塊410處判定輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入之前來執行區塊412。在此 等實例中，最初可使用第二ASR系統處理在區塊408處接收之輸入之第二語音輸入以產生第二辨識結果。接著可基於比較第二辨識結果與第一辨識結果來判定區塊408之輸入是否包括為第一語音輸入之至少一部分之重複的第二語音輸入。回應於判定輸入包括為第一語音輸入之至少一部分之重複的第二語音輸入，第二辨識結果可用於處理程序400之後續步驟。舉例而言，第二辨識結果可用以判定組合結果(例如，在區塊414處，描述如下)或第二辨識結果可用於執行基於第二辨識結果之動作(例如，在區塊416處，描述如下)。
在處理程序400之區塊414處，可基於第一辨識結果及第二辨識結果(例如，使用STT處理模組330)判定第一組合結果。在一些實例中，第一組合結果可包含第一辨識結果之至少一部分及第二辨識結果之至少一部分。舉例而言，第二語音輸入可為第一語音輸入之僅一部分之重複。在此等實例中，第一辨識結果及第二辨識結果可經組合以產生第一組合結果。在特定實例中，第一語音輸入可包括話語「奇異果中有多少卡路里？」且第一辨識結果可包括字詞序列「其醃製劑中有多少卡路里？」在此實例中，使用者可僅重複第一語音輸入之對應於第一辨識結果中之錯誤的部分。舉例而言，第二語音輸入可包括話語「奇異果中」且第二辨識結果可包括字詞序列「奇異果中。」因此，在此實例中，第一辨識結果及第二辨識結果可經組合以產生「奇異果中有多少卡路里？」之第一組合結果。
此外，在一些實例中，可藉助於ASR系統組合來組合第一辨識結果及第二辨識結果以判定第一組合結果。ASR系統組合可併入應用不同語音辨識模型及/或語音辨識引擎之多個ASR系統(例如，第一ASR系統及第二ASR系統)之辨識結果以達成較大精確性。因此，第一組合結果可比第一辨識結果及第二辨識結果中之每一者精確。ASR系統組合之實例包括辨識輸出投票錯誤減少(ROVER)、交叉適應、混淆網 路組合及晶格組合。
可在以下參考案中找到實施ASR系統組合之額外細節：J.Fiscus之「A Post-Processing System To Yield Reduced Word Error Rates：Recognizer Output Voting Error Reduction(ROVER)」(1997年之IEEE自動語音辨識及理解研討會會報中之347至354頁)；S.Stüker、C.Fügen、S.Burger及Matthias Wölfel之「Cross-system adaptation and combination for continuous speech recognition：the influence of phoneme set and acoustic front-end」(美國賓州匹茲堡，2006年9月Interspeech)；G.Evermann及P.Woodland之「Posterior Probability Decoding,Confidence Estimation And System Combination」(2000年NIST語音轉錄研討會會報)；L.Mangu、E.Brill、A.Stolcke之「Finding Consensus In Speech Recognition：Word Error Minimization And Other Applications Of Confusion Networks」(2000年計算機語音及語言14(4)，291至294頁)；A.Sankar之「Bayesian model combination(baycom)for improved recognition」(美國賓州費城，2005年4月IEEE聲學、語音及信號處理國際會議(ICASSP)，845至848頁)；所有參考案以全文引用之方式併入本文中。
在處理程序400之區塊416處，可基於第二辨識結果或第一組合結果執行動作。在區塊416處執行之動作可類似於在區塊406處執行之動作。在一些實例中，動作可包括在電子器件上顯示第二辨識結果之文字或第一組合結果之文字的至少一部分。在其他實例中，動作可包括執行滿足第一語音輸入及/或第二語音輸入中所含之使用者請求的任務。在又其他實例中，動作可包括產生概述、描述或確定藉由數位助理自第二辨識結果或第一組合結果(例如，使用自然語言處理模組332)推斷之意圖的輸出(例如，語音或文字)。
在執行區塊414之實例中，在區塊416處執行之動作可基於第一 組合結果。然而，在其他實例中，可不執行區塊414且在區塊416處執行之動作可基於第二辨識結果。在一項此類實例中，第二語音輸入可為整個第一語音輸入之重複。此外，第二辨識結果可具有足夠高之信賴度量，使得不必執行ASR系統組合。因此，在此實例中，在區塊416處執行之動作可基於第二辨識結果。
返回參考處理程序400之區塊410，可判定輸入並不包括為第一語音輸入之重複之第二語音輸入。在一些實例中，可判定輸入包括第二語音輸入，但第二語音輸入並非第一語音輸入之重複。在此等實例中，第二語音輸入可包括指示第一辨識結果中之潛在錯誤之預定話語。在一些實例中，預定話語可包括特定字詞，諸如，否、錯誤、錯、不正確、誤解、不良及其類似者。在一些實例中，預定話語可包括特定片語，諸如「再次嘗試」、「那是什麼？」、「你說什麼？」、「錯得離譜」及其類似者。在一些實例中，預定話語可包括至數位助理之玩笑式辱駡、指責或沮喪表情，諸如，「討厭！」、「什麼？！」、「惡！」、「混球」及其類似者。
在其他實例中，可在區塊410處判定在區塊408處接收之輸入並不包括任何語音輸入。舉例而言，在區塊408處接收之輸入可包括指示第一辨識結果中之潛在錯誤之非語音輸入。在一些實例中，該輸入可為可視線索之選擇。具體而言，可提供可視線索，其在經選擇時指示第一辨識結果中之潛在錯誤。在一項實例中，可視線索可為實體按鈕。在另一實例中，可視線索可為觸控式螢幕元件。觸控式螢幕元件可與諸如「報告錯誤」之文字相關聯，由此通知使用者觸控式螢幕元件之選擇將指示第一辨識結果中之潛在錯誤。
在第一辨識結果之文字之至少一部分顯示於電子器件上之實例中(例如，在區塊406處)，輸入可為所顯示文字之至少一部分之選擇。具體而言，使用者可經由觸控式螢幕(例如，觸控式螢幕246)或 其他輸入/控制器件(例如，其他輸入/控制器件248)在電子器件上醒目提示第一辨識結果之所顯示文字之至少一部分。所選擇部分亦可指示第一辨識結果之文字之與潛在錯誤相關聯之部分。
在一些實例中，在區塊408處接收之輸入可為電子器件之預定運動。預定運動可由電子器件之運動感測器(例如，運動感測器210)偵測。在一項此類實例中，預定運動可為搖動運動。此外，預定運動可具有一定運動設定檔(例如，一定移動速度、頻率及/或量值)。
在一些實例中，在區塊408處接收之輸入可為與在區塊406處執行之動作相關聯之拒絕或取消。在一項此類實例中，在區塊406處執行之動作可包括回應於「撥接Tim Carpenter」之第一語音輸入撥接「Jim Carpenter」。在此實例中，在區塊408處接收之輸入可包括取消至「Jim Carpenter」之撥接。在其他實例中，輸入可包括拒絕數位助理所提議之任務。在一項此類實例中，在區塊406執行之動作可包括基於「撥接Tim Carpenter。」之第一語音輸入輸出問題「你希望撥接Tim Carpenter嗎？」在此實例中，回應於此問題接收之輸入可包括「否」或指示拒絕該問題之類似輸入之回應。
在處理程序400之區塊418處，可提示使用者重複第一語音輸入之至少一部分。可回應於在區塊408處接收指示第一辨識結果中之潛在錯誤之輸入來執行區塊418。此外，在一些實例中，可回應於在區塊410處判定在區塊408處接收之輸入並不包括為第一語音輸入之至少一部分之重複的第二語音來執行區塊418。在區塊418處提供之提示可包括請求使用者重複第一語音輸入之至少一部分的輸出(例如，語音或文字)。舉例而言，實施於電子器件上之數位助理可藉由輸出「抱歉，能否請你重複你的請求？」來提示使用者。
區塊418可進一步包括識別第一語音輸入之與第一語音辨識中之潛在錯誤相關聯之一部分。在一些實例中，可基於第一辨識結果之信 賴度量識別第一語音輸入之與潛在錯誤相關聯之部分。詳言之，信賴度量可指示使用第一ASR系統自第一語音輸入導出之每一字詞或音素之信賴度。第一語音輸入之對應於具有小於預定值之信賴度量之字詞或音素的部分可經識別為第一語音輸入之與第一語音辨識中之潛在錯誤相關聯之部分。在特定實例中，第一語音輸入可包括「撥接Tim Carpenter」且第一辨識結果可包括文字「撥接Jim Carpenter。」此外，第一辨識結果可包括用於文字中之每一字詞之信賴度量。在此實例中，字詞「撥接」及「Carpenter」可各自具有大於預定值之信賴度量，而字詞「Jim」可具有小於預定值之信賴度量。因此，在此實例中，第一語音輸入中之部分「Tim」可經識別為與第一語音辨識中之潛在錯誤相關聯。接著可提示使用者重複第一語音輸入之經識別為與第一語音辨識中之潛在錯誤相關聯之部分。在此實例中，數位助理可藉由輸出「能否請你重複你希望撥接之人之名稱？」來提示使用者。
在處理程序400之區塊420處，可接收來自使用者之第三語音輸入。第三語音輸入可為第一語音輸入之至少一部分之重複。使用者可回應於在區塊418處提供之提示來提供第三語音輸入。第三語音輸入可隨後用於改善辨識結果且因此減小相同錯誤重現之機率。
在處理程序400之區塊422處，可使用第二ASR系統處理第三語音輸入以產生第三辨識結果。可以與區塊412處之第二語音輸入類似的方式處理第三語音輸入。在一些實例中，第三辨識結果可包括中間辨識結果，諸如音素及子字詞(例如，音節、語素及類似者)。在一些實例中，第三辨識結果可包括諸如對應於第三語音輸入之至少一部分之字詞或字詞序列的文字。此外，在一些實例中，第三辨識結果可包括用於自第三語音輸入導出之每一音素、音素序列、子字詞、字詞或字詞序列之信賴度量。
如上文所描述，第二ASR系統可與第一ASR系統相同。在其他實 例中，第二ASR系統可不同於第一ASR系統。第二ASR系統之一或多個語音辨識模型可不同於第一ASR系統之一或多個語音辨識模型。此外，第二ASR系統之一或多個語音辨識引擎可不同於第一ASR系統之一或多個語音辨識引擎。在一些實例中，第二ASR系統之錯誤率可低於第一ASR系統之錯誤率。在一些實例中，第二ASR系統之延時可大於第一ASR系統之延時。
在處理程序400之區塊424處，可基於第一辨識結果及第三辨識結果判定第二組合結果。第二組合結果可以與區塊414處之第一組合結果類似之方式判定。在一些實例中，第二組合結果可包含第一辨識結果之至少一部分及第三辨識結果之至少一部分。在一些實例中，可藉助於ASR系統組合來組合第一辨識結果及第三辨識結果以產生第二組合結果。
在處理程序400之區塊426處，可基於第三辨識結果或第二組合結果執行動作。在區塊426處執行之動作可類似於在區塊416處執行之動作。在一些實例中，動作可包括在電子器件上顯示第三辨識結果或第二組合結果之文字的至少一部分。在其他實例中，動作可包括執行滿足第一語音輸入及/或第三語音輸入中所含之使用者請求的任務。在又其他實例中，動作可包括產生概述、描述或確定藉由數位助理自第三辨識結果或第二組合結果(例如，使用自然語言處理模組332)推斷之意圖的輸出(例如，語音或文字)。在執行區塊424之實例中，在區塊426處執行之動作可基於第二組合結果。然而，在並未執行區塊424之實例中，在區塊426處執行之動作可至少部分地基於第三辨識結果。
在處理程序400之區塊428處，可使用第二ASR系統處理第一語音輸入以產生第四辨識結果。在一些實例中，可回應於在區塊408處接收指示第一辨識結果中之潛在錯誤之輸入來執行區塊428。在一些實 例中，可回應於在區塊410處判定在區塊408處接收之輸入並不包括為第一語音輸入之至少一部分之重複的第二語音輸入來執行區塊428。
可以與在區塊412處使用第二ASR系統處理第二語音輸入類似之方式使用第二ASR系統來處理第一語音輸入。在一些實例中，第二ASR系統可不同於第一ASR系統。在一項此類實例中，第二ASR系統之一或多個語音辨識引擎可不同於第一ASR系統之一或多個語音辨識引擎。在另一此類實例中，第二ASR系統之一或多個語音辨識模型可不同於第一ASR系統之一或多個語音辨識模型。在一些實例中，第二ASR系統可更精確且因此具有低於第一ASR系統之錯誤率。使用不同及更精確的ASR系統重處理第一語音輸入可係合乎需要的，以減小相同錯誤重現之機率。此可產生改善之子序列語音辨識且因此改善使用者體驗。在一些實例中，歸因於與更精確之ASR系統相關聯之較大計算成本，第二ASR系統可具有大於第一ASR系統之延時。較大延時可為用於達成更精確辨識結果及減小相同錯誤重現之機率之可接受折衷。詳言之，在經歷與辨識結果相關聯之錯誤後，使用者通常可能願意等待較久以獲得更精確結果而不是在較短時間週期內再次經歷相同錯誤。
在一些實例中，可使用第二ASR系統處理第一語音輸入之僅一部分以產生第四辨識結果。詳言之，區塊428可包括識別第一語音輸入之與第一語音辨識中之潛在錯誤相關聯之一部分。可接著使用第二ASR系統處理第一語音輸入之與潛在錯誤相關聯之部分以產生第四辨識結果。可以與區塊418中所描述類似之方式識別第一語音輸入之與潛在錯誤相關聯之部分。舉例而言，可基於第一辨識結果之信賴度量識別第一語音輸入之與潛在錯誤相關聯之部分。
在處理程序400之區塊430處，可基於第一辨識結果及第四辨識結果判定第三組合結果。第三組合結果可以與區塊414處之第一組合 結果及區塊424處之第二組合結果類似之方式判定。在一些實例中，第三組合結果可包含第一辨識結果之至少一部分及第四辨識結果之至少一部分。在一些實例中，可藉助於ASR系統組合來組合第一辨識結果及第四辨識結果以產生第三組合結果。
在處理程序400之區塊432處，可基於第四辨識結果或第三組合結果執行動作。在區塊432處執行之動作可類似於在區塊416或426處執行之動作。在一些實例中，動作可包括在電子器件上顯示第四辨識結果或第三組合結果之文字的至少一部分。在其他實例中，動作可包括執行滿足第一語音輸入及/或第四語音輸入中所含之使用者請求的任務。在又其他實例中，動作可包括產生概述、描述或確定藉由數位助理自第四辨識結果或第三組合結果(例如，使用自然語言處理模組332)推斷之意圖的輸出(例如，語音或文字)。在執行區塊430之實例中，在區塊432處執行之動作可基於第三組合結果。然而，在並未執行區塊430之實例中，在區塊432處執行之動作可至少部分地基於第四辨識結果。
雖然在圖4A至圖4B中以特定次序展示處理程序400之區塊402至432，但應瞭解，該等區塊可以任何次序執行。舉例而言，在一些實例中，區塊412可在區塊410之前或與區塊410並行執行。此外，應瞭解，在一些情況中，處理程序400之一或多個區塊可為可選的，且亦可執行額外區塊。舉例而言，在一些實例中，處理程序400可包括區塊402、404、408及412以及其餘為可選之區塊。在其他實例中，處理程序400可包括區塊402、404、408、418、420及422以及其餘為可選之區塊。在又其他實例中，處理程序400可包括區塊402、404、408及428以及其餘為可選之區塊。
5. 電子器件
圖5展示根據各種描述之實例之原理組態的電子器件500之功能 方塊圖。可藉由硬體、軟體或硬體及軟體之組合實施器件之功能區塊，以執行各種所描述實例之原理。熟習此項技術者應理解，可視情況組合圖5中所描述之功能區塊，或將其分成子區塊以實施各種所描述實例之原理。因此，本文中之描述視情況支援本文中所描述之功能區塊的任何可能組合、分離，或進一步定義。
如圖5中所示，電子器件500可包括經組態以顯示使用者介面且接收來自使用者之輸入的觸控式螢幕顯示器單元502、經組態以接收語音輸入之音訊輸入單元504、經組態以接收來自使用者之輸入之輸入單元506，及經組態以輸出音訊之揚聲器單元508。在一些實例中，音訊輸入單元504可經組態以接收來自使用者之呈聲波形式之語音輸入且將呈代表性信號形式之語音輸入傳輸至處理單元510。電子器件500可進一步包括耦接至觸控式螢幕顯示器單元502、音訊輸入單元504、輸入單元506及揚聲器單元508之處理單元510。在一些實例中，處理單元510可包括接收單元512、語音處理單元514、判定單元516、執行單元518、提示單元520及識別單元522。
處理單元510經組態以自使用者(例如，自音訊輸入單元504及使用接收單元512)接收第一語音輸入。處理單元510經組態以使用第一自動語音辨識系統(例如，使用語音處理單元514)處理第一語音輸入以產生第一辨識結果。處理單元510經組態以自使用者(例如，自輸入單元506及使用接收單元512)接收指示第一辨識結果中之潛在錯誤之輸入。該輸入可包括第二語音輸入。處理單元510經組態以使用第二自動語音辨識系統(例如，使用語音處理單元514)處理第二語音輸入以產生第二辨識結果。在一些實例中，第二語音輸入為第一語音輸入之至少一部分之重複。
在一些實例中，處理單元510經組態以(例如，使用判定單元516)判定第二語音輸入是否包含第一語音輸入之至少一部分之重複。在一 些實例中，回應於判定第二語音輸入包含第一語音輸入之至少一部分之重複，使用第二自動語音辨識系統處理第二語音輸入以產生第二辨識結果。在一些實例中，判定第二語音輸入是否包含第一語音輸入之至少一部分之重複包含判定第二語音輸入之語音轉錄相對於第一語音輸入之對應部分之語音轉錄的錯誤率是否小於預定值。在一些實例中，判定第二語音輸入是否包含第一語音輸入之至少一部分之重複包含比較第二語音輸入之音訊波形與第一語音輸入之對應部分之音訊波形。
在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於第一辨識結果之動作。在一些實例中，動作包括在電子器件上顯示第一辨識結果之文字之至少一部分。在一些實例中，第一語音輸入含有使用者請求且動作包括執行滿足該使用者請求之任務。
在一些實例中，第一自動語音辨識系統及第二自動語音辨識係同一自動語音辨識系統。在一些實例中，第一自動語音辨識系統及第二自動語音辨識系統係不同自動語音辨識系統。在一些實例中，第一自動語音辨識系統包括一或多個語音辨識模型且第二自動語音辨識系統包括不同於第一自動語音辨識系統之一或多個語音辨識模型的一或多個語音辨識模型。在一些實例中，第一自動語音辨識系統包括語音辨識引擎且第二自動語音辨識系統包括不同於第一自動語音辨識系統之語音辨識引擎之語音辨識引擎。
在一些實例中，處理單元510經組態以(例如，使用判定單元516)基於第一辨識結果及第二辨識結果判定組合結果。在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於組合結果之動作。在一些實例中，藉由使用第一辨識結果及第二辨識結果執行自動語音辨識系統組合來判定組合結果。在一些實例中，執行自動語音辨識系統組合包含實施辨識輸出投票錯誤減少、交叉適應、混淆網路組 合及晶格組合中之至少一者。
在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於第二辨識結果之動作。在一些實例中，動作包括在電子器件上顯示第二辨識結果之文字之至少一部分。在一些實例中，第一語音輸入含有使用者請求且動作包括執行滿足該使用者請求之任務。
在一些實例中，處理單元510經組態以自使用者(例如，自音訊輸入單元504及使用接收單元512)接收第一語音輸入。處理單元510經組態以使用第一自動語音辨識系統(例如，使用語音處理單元514)處理第一語音輸入以產生第一辨識結果。處理單元510經組態以自使用者(例如，自輸入單元506及使用接收單元512)接收指示第一辨識結果中之潛在錯誤之輸入。在一些實例中，處理單元510經組態以(例如，使用提示單元520及經由觸控式螢幕顯示器單元502或揚聲器單元508)提示使用者重複第一語音輸入之至少一部分。在一些實例中，處理單元510經組態以自使用者(例如，自音訊輸入單元504及使用接收單元512)接收第二語音輸入。處理單元510經組態以使用第二自動語音辨識系統(例如，使用語音處理單元514)處理第二語音輸入以產生第二辨識結果。
在一些實例中，輸入為包括預定話語之語音輸入。在一些實例中，輸入為電子器件之預定運動。在一些實例中，輸入為可視線索之選擇。在一些實例中，第一辨識結果之文字顯示於電子器件上且輸入為所顯示文字之至少一部分之選擇。在一些實例中，輸入與所提議任務之拒絕相關聯。
在一些實例中，處理單元510經組態以(例如，使用識別單元522)識別第一語音輸入之對應於第一辨識結果中之潛在錯誤之部分。在一些實例中，使用第一自動語音辨識系統處理第一語音輸入包括判定第一辨識結果之文字中之每一字詞的信賴度量。在一些實例中，基於文 字中之每一字詞之信賴度量識別第一語音輸入之與潛在錯誤相關聯之部分。在一些實例中，提示使用者重複第一語音輸入之對應於潛在錯誤之經識別部分。
在一些實例中，處理單元510經組態以執行與第一語音輸入相關聯之動作。在一些實例中，動作包括在電子器件上顯示第一辨識結果之文字之至少一部分。在一些實例中，第一語音輸入含有使用者請求且動作包括執行滿足該使用者請求之任務。
在一些實例中，第一自動語音辨識系統及第二自動語音辨識係同一自動語音辨識系統。在一些實例中，第一自動語音辨識系統及第二自動語音辨識系統係不同自動語音辨識系統。在一些實例中，第一自動語音辨識系統包括一或多個語音辨識模型且第二自動語音辨識系統包括不同於第一自動語音辨識系統之一或多個語音辨識模型的一或多個語音辨識模型。在一些實例中，第一自動語音辨識系統包括語音辨識引擎且第二自動語音辨識系統包括不同於第一自動語音辨識系統之語音辨識引擎之語音辨識引擎。
在一些實例中，處理單元510經組態以(例如，使用判定單元516)基於第一辨識結果及第二辨識結果判定組合結果。在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於組合結果之動作。在一些實例中，藉由使用第一辨識結果及第二辨識結果執行自動語音辨識系統組合來判定組合結果。在一些實例中，執行自動語音辨識系統組合包含實施辨識輸出投票錯誤減少、交叉適應、混淆網路組合及晶格組合中之至少一者。
在一些實例中，處理單元510經組態以自使用者(例如，自音訊輸入單元504及使用接收單元512)接收語音輸入。處理單元510經組態以使用第一自動語音辨識系統(例如，使用語音處理單元514)處理語音輸入以產生第一辨識結果。處理單元510經組態以自使用者(例如，自 輸入單元506及使用接收單元512)接收指示第一辨識結果中之潛在錯誤之輸入。在一些實例中，處理單元510經組態以使用第二自動語音辨識系統(例如，使用語音處理單元514)處理語音輸入以產生第二辨識結果。
在一些實例中，第二自動語音辨識系統之錯誤率低於第一自動語音辨識系統之錯誤率。在一些實例中，第二自動語音辨識系統之延時大於第一自動語音辨識系統之延時。
在一些實例中，第一自動語音辨識系統包括一或多個語音辨識模型且第二自動語音辨識系統包括不同於第一自動語音辨識系統之一或多個語音辨識模型的一或多個語音辨識模型。在一些實例中，第一自動語音辨識系統包括語音辨識引擎且第二自動語音辨識系統包括不同於第一自動語音辨識系統之語音辨識引擎之語音辨識引擎。
在一些實例中，輸入為含有預定話語之語音輸入。在一些實例中，輸入為可視線索之選擇。在一些實例中，輸入與所提議任務之拒絕相關聯。
在一些實例中，處理單元510經組態以(例如，使用識別單元522)識別語音輸入之對應於第一辨識結果中之潛在錯誤之部分。在一些實例中，使用第二自動語音辨識系統處理第一語音輸入之對應於潛在錯誤之經識別部分以產生第二辨識結果。
在一些實例中，處理單元510經組態以(例如，使用判定單元516)基於第一辨識結果及第二辨識結果判定組合結果。在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於組合結果之動作。在一些實例中，藉由使用第一辨識結果及第二辨識結果執行自動語音辨識系統組合來判定組合結果。在一些實例中，執行系統組合包含實施辨識輸出投票錯誤減少、交叉適應、混淆網路組合及晶格組合中之至少一者。
在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於第一辨識結果之動作。在一些實例中，處理單元510經組態以(例如，使用執行單元518)執行基於第二辨識結果之動作。
在一些實例中，處理單元510經組態以自使用者(例如，自音訊輸入單元504及使用接收單元512)接收第一語音輸入。處理單元510經組態以使用第一自動語音辨識系統(例如，使用語音處理單元514)處理第一語音輸入以產生第一辨識結果。處理單元510經組態以自使用者(例如，自輸入單元506及使用接收單元512)接收指示第一辨識結果中之潛在錯誤之輸入。在一些實例中，處理單元510經組態以(例如，使用判定單元516)判定輸入是否包括重複第一語音輸入之至少一部分之第二語音輸入。在一些實例中，回應於判定輸入包括重複第一語音輸入之至少一部分之第二語音輸入，處理單元510經組態以使用第二自動語音辨識系統(例如，使用語音處理單元514)處理第二語音輸入以產生第二辨識結果。在一些實例中，回應於判定輸入並不包括重複第一語音輸入之至少一部分之第二語音輸入，處理單元510經組態以(例如，使用提示單元520)提示使用者重複第一語音輸入之至少一部分，自使用者(例如，自音訊輸入單元504及使用接收單元512)接收第三語音輸入，及使用第二自動語音辨識系統(例如，使用語音處理單元514)處理第三語音輸入以產生第三辨識結果。
儘管已參考附圖充分描述實例，但應注意，各種改變及修改將變得對熟習此項技術者顯而易見。應將此等變化及修改理解為包括於如由所附申請專利範圍定義的各種實例之範疇內。
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Claims (20)

  1. 一種用於在一數位助理中處理語音之方法，該方法包含：在具有一處理器及儲存供該處理器執行之一或多個程式之記憶體之一電子器件處：接收來自一使用者之一第一語音輸入；使用一第一自動語音辨識系統處理該第一語音輸入以產生一第一辨識結果；接收來自該使用者之指示該第一辨識結果中之一潛在錯誤之一輸入，其中該輸入包括一第二語音輸入；及使用一第二自動語音辨識系統處理該第二語音輸入以產生一第二辨識結果。
  2. 如請求項1之方法，其中該第二語音輸入為該第一語音輸入之至少一部分之一重複。
  3. 如請求項1之方法，其進一步包含：判定該第二語音輸入是否包含該第一語音輸入之至少一部分之一重複，其中回應於判定該第二語音輸入包含該第一語音輸入之至少一部分之一重複，使用該第二自動語音辨識系統處理該第二語音輸入以產生該第二辨識結果。
  4. 如請求項3之方法，其中判定該第二語音輸入是否包含該第一語音輸入之至少一部分之一重複包含：判定該第二語音輸入之一語音轉錄相對於該第一語音輸入之一對應部分之一語音轉錄的一錯誤率是否小於一預定值。
  5. 如請求項3之方法，其中判定該第二語音輸入是否包含該第一語音輸入之至少一部分之一重複包含：比較該第二語音輸入之一音訊波形與該第一語音輸入之一對 應部分之一音訊波形。
  6. 如請求項1至5中任一項之方法，其進一步包含：基於該第一辨識結果及該第二辨識結果判定一組合結果。
  7. 一種用於在一數位助理中處理語音之方法，該方法包含：在具有一處理器及儲存供該處理器執行之一或多個程式之記憶體之一電子器件處：接收來自一使用者之一第一語音輸入；使用一第一自動語音辨識系統處理該第一語音輸入以產生一第一辨識結果；接收來自該使用者之指示該第一辨識結果中之一潛在錯誤之一輸入；提示該使用者重複該第一語音輸入之至少一部分；接收來自該使用者之一第二語音輸入；及使用一第二自動語音辨識系統處理該第二語音輸入以產生一第二辨識結果。
  8. 如請求項7之方法，其中該輸入為包括一預定話語之一語音輸入。
  9. 如請求項7之方法，其中該輸入為一可視線索之一選擇。
  10. 如請求項7之方法，其中該第一辨識結果之文字之至少一部分顯示於該電子器件上，且其中該輸入為該經顯示文字之至少一部分之一選擇。
  11. 如請求項7之方法，其中該輸入與一經提議任務之一拒絕相關聯。
  12. 如請求項7之方法，其進一步包含：識別該第一語音輸入之對應於該第一辨識結果中之該潛在錯誤之一部分。
  13. 如請求項12之方法，其中使用該第一自動語音辨識系統處理該第一語音輸入包括判定該第一辨識結果之一文字中之每一字詞的一信賴度量，且其中基於該文字中之每一字詞之該信賴度量識別該第一語音輸入之與該潛在錯誤相關聯之該部分。
  14. 如請求項12之方法，其中提示該使用者重複該第一語音輸入之對應於該潛在錯誤之該經識別部分。
  15. 一種用於在一數位助理中處理語音之方法，該方法包含：在具有一處理器及儲存供該處理器執行之一或多個程式之記憶體之一電子器件處：接收來自一使用者之一語音輸入；使用一第一自動語音辨識系統處理該語音輸入以產生一第一辨識結果；接收來自該使用者之指示該第一辨識結果中之一潛在錯誤之一輸入；及使用一第二自動語音辨識系統處理該語音輸入之至少一部分以產生一第二辨識結果。
  16. 如請求項15之方法，其中該第二自動語音辨識系統之一錯誤率低於該第一自動語音辨識系統之一錯誤率。
  17. 如請求項15之方法，其中該第二自動語音辨識系統之一延時大於該第一自動語音辨識系統之一延時。
  18. 如請求項15之方法，其進一步包含：識別該語音輸入之對應於該第一辨識結果中之該潛在錯誤之一部分，其中使用該第二自動語音辨識系統處理該語音輸入之對應於該潛在錯誤之該經識別部分以產生該第二辨識結果。
  19. 一種用於處理語音之電腦可讀儲存媒體，該電腦可讀儲存媒體包含用於使一或多個處理器執行以下操作之指令： 接收來自一使用者之一第一語音輸入；使用一第一自動語音辨識系統處理該第一語音輸入以產生一第一辨識結果；接收來自該使用者之指示該第一辨識結果中之一潛在錯誤之一輸入，其中該輸入包括一第二語音輸入；及使用一第二自動語音辨識系統處理該第二語音輸入以產生一第二辨識結果。
  20. 一種用於處理語音之電子器件，該電子器件包含：一或多個處理器；記憶體；一或多個程式，其中該一或多個程式儲存於該記憶體中且經組態以由該一或多個處理器執行，該一或多個程式包括用於執行以下操作之指令：接收來自一使用者之一第一語音輸入；使用一第一自動語音辨識系統處理該第一語音輸入以產生一第一辨識結果；接收來自該使用者之指示該第一辨識結果中之一潛在錯誤之一輸入，其中該輸入包括一第二語音輸入；及使用一第二自動語音辨識系統處理該第二語音輸入以產生一第二辨識結果。
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