<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>261</id>
    <completedYear>2014</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>VDE VERLAG GMBH</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Semi-Automatic Calibration for Dereverberation by Spectral Subtraction for Continuous Speech Recognition</title>
    <abstract language="eng">In this article, we describe a semi-automatic calibration algorithm for dereverberation by spectral subtraction. We verify the method by a comparison to a manual calibration derived from measured room impulse responses (RIR). We conduct extensive experiments to understand the effect of all involved parameters and to verify values suggested in the literature. The experiments are performed on a text read by 31 speakers and recorded by a headset and three far-field microphones. Results are measured in terms of automatic speech recognition (ASR) performance using a 1-gram model to emphasize acoustic recognition performance. To accommodate for the acoustic change by dereverberation we apply supervised MAP adaptation to the hidden Markov model output probabilities. The combination of dereverberation and adaptation yields a relative improvement of about 35% in terms of word error rate (WER) compared to the original signal.</abstract>
    <parentTitle language="eng">ITG Symposium on Speech Communication 2014, Erlangen.</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Korbinian Riedhammer</author>
    <author>Tobias Bocklet</author>
    <author>Juan Rafael Orozco-Arroyave</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>270</id>
    <completedYear>2012</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>8349</pageFirst>
    <pageLast>8353</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Software Kit for Automatic Voice Descrambling</title>
    <abstract language="eng">Voice scrambling is widely used to add privacy to the radio communication of various authorities - but is also used by criminals to evade prosecution. In this article, we consider various analog voice scrambling techniques such as fixed frequency inversion, splitband inversion and rolling code scramblers. We explain how to break them using automatically extracted measures and scoring algorithms, and evaluate the proposed system using simulated data. While the simple inversion can be easily broken, the more advanced techniques require additional work prior to unsupervised automatization; the presented user interface allows the user to refine the automatic results to obtain a high quality solution.</abstract>
    <parentTitle language="eng">2012 IEEE International Conference on Communications (ICC); IEEE International Workshop on Security and Forensics in Communication Systems (SFCS).</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Korbinian Riedhammer</author>
    <author>Martin Ring</author>
    <author>Elmar Nöth</author>
    <author>Daniel Kolb</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>273</id>
    <completedYear>2011</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Novel Lecture Browser Using Key Phrases and Stream Graphs</title>
    <abstract language="deu">We present a novel lecture browser that utilizes ranked key phrases displayed on a stream graph to overcome the shortcomings of traditional extractive (query-based) summaries. The system extracts key phrases from the ASR transcripts, performs an unsupervised ranking, and displays an initial number of phrases on the stream graph. This graph gives an intuition of when which key phrase is spoken, and how dominant it is throughout the lecture. The user can select the phrases to be displayed and furthermore adjust the ranking of the all phrases. All user interactions are logged to a server to improve the ranking algorithms and provide user specific rankings.</abstract>
    <parentTitle language="deu">Lehrstuhl für Mustererkennung, Universität Erlangen-Nürnberg</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Korbinian Riedhammer</author>
    <author>Martin Gropp</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>279</id>
    <completedYear>2010</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4226</pageFirst>
    <pageLast>4229</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Clap Your Hands! Calibrating Spectral Subtraction for Dereverberation</title>
    <abstract language="eng">Reverberation effects as observed by room microphones severely degrade the performance of automatic speech recognition systems. We investigate the use of dereverberation by spectral subtraction as proposed by Lebart and Boucher and introduce a simple approach to estimate the required decay parameter by clapping hands. Experiments on small vocabulary continuous speech recognition task on read speech show that using the calibrated dereverberation improves WER from 73.2 to 54.7 for the best microphone. In combination with system adaptation, the WER could be reduced to 28.2, which is only a 16% relative loss of performance comparison to using a headset instead of a room microphone.</abstract>
    <parentTitle language="eng">2010 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), Dallas, TX, USA, March 2010.</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Uwe Zäh</author>
    <author>Korbinian Riedhammer</author>
    <author>Tobias Bocklet</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>robustness</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>280</id>
    <completedYear>2010</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2420</pageFirst>
    <pageLast>2423</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-03-20</completedDate>
    <publishedDate>2018-06-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FAU IISAH Corpus – A German Speech Database Consisting of Human-Machine and Human-Human Interaction Acquired by Close-Talking and Far-Distance Microphones</title>
    <parentTitle language="eng">7th Conference on International Language Resources and Evaluation (LREC 2010), European Language Resources Association (ELRA), Valletta, Malta,  May 2010.</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Werner Spiegl</author>
    <author>Korbinian Riedhammer</author>
    <author>Stefan Steidl</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>281</id>
    <completedYear>2009</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2015</pageFirst>
    <pageLast>2018</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation>ACM New York, NY, USA ©2009</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards a Language-independent Intelligibility Assessment of Children with Cleft Lip and Palate</title>
    <abstract language="eng">We describe a novel evaluation system for the intelligibility assessment of children with CLP on standardized tests. The system is solely based on standard cepstral features in form of MFCCs. No other information like word alignments is used. So the system can be easily adapted to other languages. For each child one GMM is created by adaptation of a UBM to the speaker-specific MFCCs. The components of this GMM are concatenated in order to create a so-called GMM supervector. These GMM supervectors are then used as meta features for an SVR. We evaluated our language-independent system on two different datasets of children suffering from CLP. One dataset contains recordings of 35 German children, where the children named different pictograms. The other dataset contains recordings of 14 Italian speaking children, who repeated standardized sentences. On both datasets we achieved high correlations: up to 0.81 for the German dataset and 0.83 for the Italian dataset.</abstract>
    <parentTitle language="eng">WOCCI '09 Proceedings of the 2nd Workshop on Child, Computer and Interaction (WOCCI 2009), Cambridge, Massachusetts, November 2009, Article No. 6.</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Tobias Bocklet</author>
    <author>Andreas Maier</author>
    <author>Korbinian Riedhammer</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>294</id>
    <completedYear>2006</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>17</pageFirst>
    <pageLast>22</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic Evaluation of Tracheoesophageal Telephone Speech</title>
    <abstract language="eng">The tracheoesophageal (TE) substitute voice is currently state–of–the–art treatment to restore the ability to speak after laryngectomy. The intelligibility while talking over a telephone is an important clinical factor, as it is a crucial part of the patients’ social life. An objective way to rate the intelligibility of substitute voices when talking over a telephone is desirable to improve the post–laryngectomy speech therapy. An automatic speech recognition (ASR) system was applied to 41 high quality recordings of post–laryngectomy patients. The ASR system was trained with normal, non–pathologic speech. It yielded a word accuracy (WA) of 36.9%±18.0%; compared to the intelligibility rating of a group of human experts the ASR system had a correlation coefﬁcient of -.88. After downsampling the 41 recordings to telephone quality, the ASR system reached a WA of 26.4%±13.9% leading to a correlation coefﬁcient of -.80. These results conﬁrm that an ASR system can be used for objective intelligibility rating over the telephone.</abstract>
    <parentTitle language="eng">First International Language Technologies Conference (IS-LTC 2006), Ljubljana, Slovenia, October 2006.</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Korbinian Riedhammer</author>
    <author>Tino Haderlein</author>
    <author>Maria Schuster</author>
    <author>Frank Rosanowski</author>
    <author>Elmar Nöth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>307</id>
    <completedYear>2012</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>390</pageFirst>
    <pageLast>397</pageLast>
    <pageNumber/>
    <edition/>
    <issue>26(3)</issue>
    <volume/>
    <type>contributiontoperiodical</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic Intelligibility Assessment of Speakers After Laryngeal Cancer by Means of Acoustic Modeling</title>
    <abstract language="eng">One aspect of voice and speech evaluation after laryngeal cancer is acoustic analysis. Perceptual evaluation by expert raters is a standard in the clinical environment for global criteria such as overall quality or intelligibility. So far, automatic approaches evaluate acoustic properties of pathologic voices based on voiced/unvoiced distinction and fundamental frequency analysis of sustained vowels. Because of the high amount of noisy components and the increasing aperiodicity of highly pathologic voices, a fully automatic analysis of fundamental frequency is difficult. We introduce a purely data-driven system for the acoustic analysis of pathologic voices based on recordings of a standard text.</abstract>
    <parentTitle language="eng">Journal of Voice</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Tobias Bocklet</author>
    <author>Korbinian Riedhammer</author>
    <author>Elmar Nöth</author>
    <author>Ulrich Eysholdt</author>
    <author>Tino Haderlein</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Speech Recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lung cancer technologies</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
</export-example>
