1. A spiral-wound gasket for use between facing flanges of two flow conduit sections, each flow conduit section defining a through bore, said spiral-wound gasket comprising:
an outer ring;
a metal inner ring having an annular inner face which is flush with said through bores, said inner ring having grooves and ridges along upper and lower surfaces of said inner ring and a coating directly on said grooves and said ridges; and
a low-compression, spiral-wound portion positioned between said outer ring and said inner ring, said spiral-wound portion having an alternating sequence of metal windings and polymeric sealant strips, wherein each metal winding having a first axial width dimension and each sealant strip having a second axial width dimension, said second axial width dimension is larger than said first axial width dimension, wherein said second axial width dimension is between about 0.175 inches and about 0.185 inches and said first axial width dimension is between about 0.145 inches and about 0.165 inches.
2. The spiral-wound gasket of claim 1 wherein said outer ring has a thickness dimension and said second axial width dimension being larger than said thickness dimension.
3. The spiral-wound gasket of claim 1 wherein said coating is flexible graphite or PTFE.
4. The spiral-wound gasket of claim 1 wherein each metal winding has an exposed end surface and each sealant strip has an axially extending end portion which is constructed and arranged without any overlap with any one of said exposed end surfaces prior to compression.
5. The spiral-wound gasket of claim 4 wherein each axially extending end portion of said alternating sequence is in direct contact with said facing flanges during compression.
6. The spiral-wound gasket of claim 2 wherein said thickness dimension is between 0.115 inches and 0.131 inches.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A Viterbi decoder, comprising:
an observation vector sequence generator for generating an observation vector sequence by converting an input speech to a sequence of observation vectors;
a local optimal state calculator for obtaining, among states constituting a hidden Markov model, a partial state sequence having a maximum similarity up to a current observation vector as an optimal state;
an observation probability calculator for obtaining, as a current observation probability, a probability for observing the current observation vector in the optimal state;
a buffer for storing therein a specific number of previous observation probabilities;
a non-linear filter for calculating a filtered probability by using the previous observation probabilities stored in the buffer and the current observation probability; and
a maximum likelihood calculator for calculating a partial maximum likelihood by using the filtered probability
2. The Viterbi decoder of claim 1, wherein the observation probability calculator updates the buffer using the current observation probability.
3. The Viterbi decoder of claim 1, wherein the non-linear filter calculates, as the filtered probability, a maximum value of the previous observation probabilities stored in the buffer and the current observation probability.
4. The Viterbi decoder of claim 1, wherein the non-linear filter calculates, as the filtered probability, a mean value of the previous observation probabilities stored in the buffer and the current observation probability.
5. The Viterbi decoder of claim 1, wherein the non-linear filter calculates, as the filtered probability, a median value of the previous observation probabilities stored in the buffer and the current observation probability.
6. The Viterbi decoder of claim 1, wherein the non-linear filter calculates the filtered probability by using correlations between the previous observation probabilities stored in the buffer and the current observation probability.
7. A speech recognition method using a Viterbi decoder, the method comprising:
generating an observation vector sequence by converting an input speech to a sequence of observation vectors;
obtaining, among states constituting a hidden Markov model, a partial state sequence having a maximum similarity up to a current observation vector as an optimal state;
obtaining, as a current observation probability, a probability for observing the current observation vector in the optimal state;
calculating a filtered probability by using previous observation probabilities and the current observation probability;
calculating a partial maximum likelihood by using the filtered probability;
updating a cumulative maximum likelihood by using the partial maximum likelihood; and
outputting a recognition result for the input speech based on the cumulative maximum likelihood,
wherein said obtaining the optimal state, said obtaining the current observation probability, said calculating the filtered probability, said calculating the partial maximum likelihood and said updating the cumulative maximum likelihood are repeated until it reaches the last observation vector in the observation vector sequence.
8. The speech recognition method of claim 7, wherein the filtered probability is a maximum value of the previous observation probabilities and the current observation probability.
9. The speech recognition method of claim 7, wherein the filtered probability is a mean value of the previous observation probabilities and the current observation probability.
10. The speech recognition method of claim 7, wherein the filtered probability is a median value of the previous observation probabilities and the current observation probability.
11. The speech recognition method of claim 7, wherein the filtered probability is calculated by using correlations between the previous observation probabilities and the current observation probability.