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While they facilitate rapid and natural interaction in the form of voice commands, current speech interfaces lack natural methods for command correction. We present ScratchThat, a method for supporting command-agnostic speech repair in voice-driven assistants, suitable for enabling corrective functionality within third-party commands. Unlike existing speech repair methods, ScratchThat is able to automatically infer query parameters and intelligently select entities in a correction clause for editing. We conducted three evaluations to (1) elicit natural forms of speech repair in voice commands, (2) compare the interaction speed and NASA TLX score of the system to existing voice-based correction methods, and (3) assess the accuracy of the ScratchThat algorithm. Our results show that (1) speech repair for voice commands differ from previous models for conversational speech repair, (2) methods for command correction based on speech repair are significantly faster than other voice-based methods, and (3) the ScratchThat algorithm facilitates accurate command repair as rated by humans (77% accuracy) and machines (0.94 BLEU score). Finally, we present several ScratchThat use cases, which collectively demonstrate its utility across many applications.<\/jats:p>","DOI":"10.1145\/3328934","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T13:45:01Z","timestamp":1561383901000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["ScratchThat"],"prefix":"10.1145","volume":"3","author":[{"given":"Jason","family":"Wu","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karan","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Li","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"Chen","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey","family":"Bigham","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"{n. d.}. 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Accessed: 2019-04-11."},{"key":"e_1_2_2_6_1","unstructured":"{n. d.}. Dragon Revising text. https:\/\/www.nuance.com\/products\/help\/dragon\/dragon-for-mac\/enx\/Content\/Correction\/RevisingText.htm. Accessed: 2019-04-11.  {n. d.}. Dragon Revising text. https:\/\/www.nuance.com\/products\/help\/dragon\/dragon-for-mac\/enx\/Content\/Correction\/RevisingText.htm. Accessed: 2019-04-11."},{"volume-title":"Principle-based parsing","author":"Abney Steven P","key":"e_1_2_2_7_1","unstructured":"Steven P Abney . 1991. Parsing by chunks . In Principle-based parsing . Springer , 257--278. Steven P Abney. 1991. Parsing by chunks. In Principle-based parsing. 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Multilingual Disfluency Removal using NMT."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.121"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174047"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219885"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3027063.3053166"},{"key":"e_1_2_2_18_1","volume-title":"Affect-lm: A neural language model for customizable affective text generation. arXiv preprint arXiv:1704.06851","author":"Ghosh Sayan","year":"2017","unstructured":"Sayan Ghosh , Mathieu Chollet , Eugene Laksana , Louis-Philippe Morency , and Stefan Scherer . 2017 . Affect-lm: A neural language model for customizable affective text generation. arXiv preprint arXiv:1704.06851 (2017). Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017. 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Boris Katz Gary Borchardt Sue Felshin and Federico Mora. 2018. A Natural Language Interface for Mobile Devices. (2018).","DOI":"10.1002\/9781118976005.ch23"},{"key":"e_1_2_2_27_1","volume-title":"The Hungarian method for the assignment problem. Naval research logistics quarterly 2, 1-2","author":"Kuhn Harold W","year":"1955","unstructured":"Harold W Kuhn . 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly 2, 1-2 ( 1955 ), 83--97. Harold W Kuhn. 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly 2, 1-2 (1955), 83--97."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1029632.1029674"},{"key":"e_1_2_2_29_1","first-page":"16","article-title":"Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition","volume":"94","author":"Mauldin Michael L","year":"1994","unstructured":"Michael L Mauldin . 1994 . Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition . In AAAI , Vol. 94. 16 -- 21 . Michael L Mauldin. 1994. Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition. In AAAI, Vol. 94. 16--21.","journal-title":"AAAI"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173580"},{"key":"e_1_2_2_31_1","volume-title":"Interactive Digital","author":"O\u00e2\u0102 Daniel","year":"2009","unstructured":"Daniel O\u00e2\u0102 &Zacute;Sullivan. 2009. Using an adaptive voice user interface to gain efficiencies in automated calls. White Paper , Interactive Digital , Smithtown , USA ( 2009 ). Daniel O\u00e2\u0102&Zacute;Sullivan. 2009. Using an adaptive voice user interface to gain efficiencies in automated calls. White Paper, Interactive Digital, Smithtown, USA (2009)."},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236112.3236130"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2017.7917645"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161187"},{"key":"e_1_2_2_35_1","first-page":"3776","article-title":"Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models","volume":"16","author":"Serban Iulian Vlad","year":"2016","unstructured":"Iulian Vlad Serban , Alessandro Sordoni , Yoshua Bengio , Aaron C Courville , and Joelle Pineau . 2016 . Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models .. In AAAI , Vol. 16. 3776 -- 3784 . Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016. Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models.. 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