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Cross-lingual acoustic modeling in upper sorbian - preliminary study

: Kraljevski, Ivan; Rjelka, Marek; Duckhorn, Frank; Tschöpe, Constanze; Wolff, Matthias

Volltext urn:nbn:de:0011-n-6332972 (157 KByte PDF)
MD5 Fingerprint: 3268477ea551f6f227bd9d04cdfcc7a1
Erstellt am: 18.3.2021

Hillmann, S. ; TU Berlin:
Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz, Berlin, 3. - 5. März 2021
Dresden: TUDpress, 2021 (Studientexte zur Sprachkommunikation 99)
ISBN: 978-3-95908-227-3
8 S.
Konferenz "Elektronische Sprachsignalverarbeitung" (ESSV) <32, 2021, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IKTS ()
acoustic modeling; speech recognition; Upper Sorbian

In this paper, we present a preliminary study for acoustic modeling in Upper Sorbian, where a model of German was used in cross-lingual transfer learning. At first, we define the grapheme and phoneme inventories and map the target phonemes from the most similar German source equivalents. Phonetically balanced sentences for the recording prompts were selected from a combination of general and domain-specific textual data. The speech corpora with a total duration of around 11 hours was collected in controlled recording sessions involving an equal number of females, males, and children. The baseline acoustic model was employed to force-align the speech corpora given the knowledge-based phoneme mappings. The goodness of the mappings was evaluated by the phoneme confusions in free-phoneme recognition. The new derived data-driven model with a reduced phoneme set was included in the adaptation and evaluation along with the baseline acoustic model. The model adaptation performance was cross-validated with the “Leave One Group Out” strategy. We observed major improvements in phoneme error rates after adaptation for the knowledge-based and data-driven phoneme mappings. The study confirmed the feasibility of transfer learning for acoustic model adaptation in the case of Upper Sorbian, at the same time demonstrating practical usability with a small vocabulary speech recognition application (Smart Lamp).