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- Version:
- 2.0, 25. Feb. 1992
- Hardware / OS requirements:
- Low Level SESAM Workstation
- Description:
-
SAM_SPEX provides an analytic basis for the definition of
recogniser performance. The SAM project has adopted six speaker
dependent speech parameter defined within the British Alvey
project, MMI 132 STA. SAM_SPEX version 2.0 implements the
extraction of these parameters from isolated words.
The parameters measured are:
- Speaking Rate: The duration of each token is calculated
using an energy-based endpoint algorithm (time resolution
1msec) and related to the mean duration for all tokens of
the same utterance.
- Energy: The ratio of the mean energy and the peak energy
of the utterance.
- Pitch Frequency:
The mean and variance of the pitch frequency estimated within
the voiced parts of the endpointed utterance using a standard
SIFT (Simple Inverse Filtering Tracking) algorithm.
- Voice Quality: The ratio of the energy between 0-2kHz
and 0-fs/2Hz (fs = sampling frequency).
- Vocal Tract Area Distance: Calculated in two steps:
- The average area vector is computed.
- The vocal tract area distance is computed as the Euclidean
distance between the average vocal tract area vector and the
average of all vocal tract area vectors for tokens of the same
utterance.
- Pattern Congruence: Calculated in two steps:
- The output values of an eighteen channels filter bank are
computed every 10msec of a token.
- A dynamic time warping algorithm is used to calculate the
distance between the token and all other tokens of the same utterance.
At present the definition of the parameters is under review and
an improved definition is in preparation.
- Developing lab:
-
Jydsk Telefon
Sletvej 30
DK - 8310 Tranbjerg - J
Denmark
Tel: (45) 86 29 33 66
Fax: (45) 86 29 90 68
- Contact:
- Sven Danielsen
- NB:
- This software is currently only available to SAM
partners as the definition of the parameters is under review and
an improved definition is in preparation.
Next: SAMTRA (SAM TRanscription Analysis)
Up: SAM software tools
Previous: SAM_SLM
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